hypergraph/hypergraph.json

{
  "nodes": [
    {
      "id": "alg-majority-voting",
      "name": "Majority Voting / Answer Aggregation",
      "type": "AlgorithmicComponent",
      "description": "Selecting the most frequent/consistent answer across paths",
      "attributes": {
        "subtype": "voting"
      }
    },
    {
      "id": "alg-tree-search",
      "name": "Tree Search (BFS/DFS)",
      "type": "AlgorithmicComponent",
      "description": "Exploring branching paths through a tree of partial solutions",
      "attributes": {
        "subtype": "search"
      }
    },
    {
      "id": "alg-backtracking",
      "name": "Backtracking",
      "type": "AlgorithmicComponent",
      "description": "Reverting to a previous state when a path is unpromising",
      "attributes": {
        "subtype": "search"
      }
    },
    {
      "id": "alg-code-execution",
      "name": "External Code Execution",
      "type": "AlgorithmicComponent",
      "description": "Running generated code via an interpreter for computation (Python, SQL)",
      "attributes": {
        "subtype": "execution"
      }
    },
    {
      "id": "alg-retrieval",
      "name": "External Knowledge Retrieval",
      "type": "AlgorithmicComponent",
      "description": "Fetching documents/passages from external knowledge sources",
      "attributes": {
        "subtype": "retrieval"
      }
    },
    {
      "id": "alg-tool-use",
      "name": "External Tool Integration",
      "type": "AlgorithmicComponent",
      "description": "Calling external tools (search, calculators, APIs) during reasoning",
      "attributes": {
        "subtype": "execution"
      }
    },
    {
      "id": "alg-clustering",
      "name": "Clustering (K-Means / Sentence-BERT)",
      "type": "AlgorithmicComponent",
      "description": "Grouping questions/examples by similarity for representative sampling",
      "attributes": {
        "subtype": "analysis"
      }
    },
    {
      "id": "alg-multiple-sampling",
      "name": "Multiple Path Sampling",
      "type": "AlgorithmicComponent",
      "description": "Generating multiple distinct reasoning chains via temperature/nucleus sampling",
      "attributes": {
        "subtype": "sampling"
      }
    },
    {
      "id": "alg-uncertainty-estimation",
      "name": "Uncertainty Estimation",
      "type": "AlgorithmicComponent",
      "description": "Measuring model uncertainty to select examples or prompts",
      "attributes": {
        "subtype": "analysis"
      }
    },
    {
      "id": "alg-auto-prompt-gen",
      "name": "Automated Prompt Generation / Optimization",
      "type": "AlgorithmicComponent",
      "description": "Automatically generating, scoring, and selecting candidate prompts",
      "attributes": {
        "subtype": "optimization"
      }
    },
    {
      "id": "alg-kg-reasoning",
      "name": "Knowledge Graph / Subgraph Reasoning",
      "type": "AlgorithmicComponent",
      "description": "Reasoning over knowledge graph structures for retrieval or inference",
      "attributes": {
        "subtype": "retrieval"
      }
    },
    {
      "id": "alg-thought-evaluation",
      "name": "Thought / State Evaluation",
      "type": "AlgorithmicComponent",
      "description": "Assessing quality or promise of intermediate reasoning states (heuristic or LLM)",
      "attributes": {
        "subtype": "analysis"
      }
    },
    {
      "id": "alg-boosting",
      "name": "AdaBoost-Style Prompt Boosting",
      "type": "AlgorithmicComponent",
      "description": "Iteratively selecting/weighting prompts using boosting algorithms",
      "attributes": {
        "subtype": "optimization"
      }
    },
    {
      "id": "alg-physics-simulation",
      "name": "Physics Engine Simulation",
      "type": "AlgorithmicComponent",
      "description": "Running physical simulations (e.g., MuJoCo) to ground reasoning",
      "attributes": {
        "subtype": "execution"
      }
    },
    {
      "id": "alg-lmulator",
      "name": "LMulator (LM-Emulated Execution)",
      "type": "AlgorithmicComponent",
      "description": "Using the LLM itself to simulate code execution for non-executable parts",
      "attributes": {
        "subtype": "execution"
      }
    },
    {
      "id": "pc-intermediate-reasoning",
      "name": "Intermediate Reasoning Steps",
      "type": "PromptComponent",
      "description": "Generating explicit step-by-step reasoning in natural language",
      "attributes": {
        "subtype": "formatting"
      }
    },
    {
      "id": "pc-symbolic-representation",
      "name": "Symbolic / Formal Representation",
      "type": "PromptComponent",
      "description": "Replacing NL with condensed symbols, formal logic, or algebraic variables",
      "attributes": {
        "subtype": "formatting"
      }
    },
    {
      "id": "pc-contrastive-examples",
      "name": "Contrastive Examples (Positive + Negative)",
      "type": "PromptComponent",
      "description": "Providing both correct and incorrect reasoning demonstrations",
      "attributes": {
        "subtype": "examples"
      }
    },
    {
      "id": "pc-emotional-stimulus",
      "name": "Emotional Stimulus / Affective Cues",
      "type": "PromptComponent",
      "description": "Appending emotionally charged phrases to enhance performance",
      "attributes": {
        "subtype": "stimulus"
      }
    },
    {
      "id": "pc-question-rephrasing",
      "name": "Question Rephrasing / Clarification",
      "type": "PromptComponent",
      "description": "Having the LLM rephrase/expand a question for better comprehension",
      "attributes": {
        "subtype": "formatting"
      }
    },
    {
      "id": "pc-concise-reasoning",
      "name": "Concise / Draft Reasoning (Token Minimization)",
      "type": "PromptComponent",
      "description": "Generating minimal, information-dense reasoning steps",
      "attributes": {
        "subtype": "constraint"
      }
    },
    {
      "id": "pc-analogical-examples",
      "name": "Self-Generated Analogical Examples",
      "type": "PromptComponent",
      "description": "LLM generates similar solved examples before tackling the target",
      "attributes": {
        "subtype": "examples"
      }
    },
    {
      "id": "pc-complexity-selection",
      "name": "Complexity-Based Example Selection",
      "type": "PromptComponent",
      "description": "Selecting exemplars with more reasoning steps as demonstrations",
      "attributes": {
        "subtype": "examples"
      }
    },
    {
      "id": "pc-role-assignment",
      "name": "Role / Persona Assignment",
      "type": "PromptComponent",
      "description": "Assigning a specific role or persona to guide LLM behaviour",
      "attributes": {
        "subtype": "instruction"
      }
    },
    {
      "id": "pc-task-instruction",
      "name": "Explicit Task Instruction",
      "type": "PromptComponent",
      "description": "Clear specification of intent, domain, and output format",
      "attributes": {
        "subtype": "instruction"
      }
    },
    {
      "id": "pc-propositional-logic",
      "name": "Propositional Logic Extraction",
      "type": "PromptComponent",
      "description": "Extracting logical propositions and applying formal logic laws",
      "attributes": {
        "subtype": "formatting"
      }
    },
    {
      "id": "pc-narrative-construction",
      "name": "Narrative Construction (Temporal Ordering)",
      "type": "PromptComponent",
      "description": "Building temporally grounded narratives to structure reasoning",
      "attributes": {
        "subtype": "formatting"
      }
    },
    {
      "id": "pc-code-generation",
      "name": "Code Generation (Prompt Pattern)",
      "type": "PromptComponent",
      "description": "Expressing reasoning as executable code / pseudocode within the prompt",
      "attributes": {
        "subtype": "formatting"
      }
    },
    {
      "id": "pc-template-reuse",
      "name": "Thought-Template Reuse",
      "type": "PromptComponent",
      "description": "Retrieving and instantiating high-level reusable reasoning patterns",
      "attributes": {
        "subtype": "examples"
      }
    },
    {
      "id": "pc-context-filtering",
      "name": "Context Regeneration / Irrelevant Info Filtering",
      "type": "PromptComponent",
      "description": "Rewriting the input context to remove distracting information",
      "attributes": {
        "subtype": "constraint"
      }
    },
    {
      "id": "pc-role-assignment",
      "name": "Role Assignment / Persona",
      "type": "PromptComponent",
      "description": "Assigning a specific role or persona to the LLM to guide its behavior",
      "attributes": {
        "subtype": "instruction"
      }
    },
    {
      "id": "pc-task-instruction",
      "name": "Explicit Task Instruction",
      "type": "PromptComponent",
      "description": "Clear, explicit instruction describing the task to be performed",
      "attributes": {
        "subtype": "instruction"
      }
    },
    {
      "id": "pc-format-constraints",
      "name": "Output Format Constraints",
      "type": "PromptComponent",
      "description": "Specifying structured output format (outline, bullet points, JSON)",
      "attributes": {
        "subtype": "formatting"
      }
    },
    {
      "id": "pc-algebraic-abstraction",
      "name": "Algebraic Abstraction + Multi-Run Validation",
      "type": "PromptComponent",
      "description": "Replacing numerics with variables, solving symbolically, validating with multiple inputs",
      "attributes": {
        "subtype": "formatting"
      }
    },
    {
      "id": "df-decomposition",
      "name": "Problem Decomposition",
      "type": "DataFlow",
      "description": "Breaking a complex problem into smaller sub-problems",
      "attributes": {
        "subtype": "decomposition"
      }
    },
    {
      "id": "df-sequential-chaining",
      "name": "Sequential Sub-Problem Chaining",
      "type": "DataFlow",
      "description": "Solving sub-problems in order where each answer feeds the next",
      "attributes": {
        "subtype": "chaining"
      }
    },
    {
      "id": "df-verification",
      "name": "Verification / Self-Verification",
      "type": "DataFlow",
      "description": "Checking correctness of generated reasoning or answers",
      "attributes": {
        "subtype": "verification"
      }
    },
    {
      "id": "df-self-reflection",
      "name": "Self-Reflection / Self-Critique",
      "type": "DataFlow",
      "description": "Model critiques its own output to identify flaws",
      "attributes": {
        "subtype": "feedback"
      }
    },
    {
      "id": "df-iterative-refinement",
      "name": "Iterative Refinement / Feedback Loop",
      "type": "DataFlow",
      "description": "Repeatedly improving output through generate-critique-revise cycles",
      "attributes": {
        "subtype": "feedback"
      }
    },
    {
      "id": "df-context-augmentation",
      "name": "Context Augmentation",
      "type": "DataFlow",
      "description": "Enriching the prompt with retrieved / external information before generation",
      "attributes": {
        "subtype": "aggregation"
      }
    },
    {
      "id": "df-action-observation",
      "name": "Action-Observation Loop",
      "type": "DataFlow",
      "description": "Interleaving LLM reasoning actions with environment observations",
      "attributes": {
        "subtype": "chaining"
      }
    },
    {
      "id": "df-document-scoring",
      "name": "Document Relevance Scoring / Note-Taking",
      "type": "DataFlow",
      "description": "Systematically evaluating relevance of retrieved documents",
      "attributes": {
        "subtype": "filtering"
      }
    },
    {
      "id": "df-knowledge-adaptation",
      "name": "Knowledge Domain ID + Dynamic Adaptation",
      "type": "DataFlow",
      "description": "Identifying relevant knowledge domains and adapting reasoning from them",
      "attributes": {
        "subtype": "aggregation"
      }
    },
    {
      "id": "df-graph-reasoning",
      "name": "Graph-Based Reasoning Flow",
      "type": "DataFlow",
      "description": "Modeling reasoning as a directed graph with merging/splitting paths",
      "attributes": {
        "subtype": "chaining"
      }
    },
    {
      "id": "df-tabular-reasoning",
      "name": "Tabular Reasoning / Table Operations",
      "type": "DataFlow",
      "description": "Operating on structured tables (add columns, sort, filter, group_by)",
      "attributes": {
        "subtype": "chaining"
      }
    },
    {
      "id": "df-external-memory",
      "name": "External Memory / Scratchpad",
      "type": "DataFlow",
      "description": "Using an external workspace to store and retrieve intermediate results",
      "attributes": {
        "subtype": "storage"
      }
    },
    {
      "id": "df-reductio-ad-absurdum",
      "name": "Reductio ad Absurdum Verification",
      "type": "DataFlow",
      "description": "Verifying a step by checking if its negation leads to contradiction",
      "attributes": {
        "subtype": "verification"
      }
    },
    {
      "id": "df-event-extraction",
      "name": "Event Extraction + Generalization + Filtering",
      "type": "DataFlow",
      "description": "Extracting events, generalizing, chronologically filtering for coverage",
      "attributes": {
        "subtype": "filtering"
      }
    },
    {
      "id": "df-aspect-opinion-sentiment",
      "name": "Aspect → Opinion → Sentiment Chain",
      "type": "DataFlow",
      "description": "Multi-hop reasoning through aspect identification, opinion determination, sentiment inference",
      "attributes": {
        "subtype": "chaining"
      }
    },
    {
      "id": "df-reasoning-harmonization",
      "name": "Reasoning Unification / Harmonization",
      "type": "DataFlow",
      "description": "Iteratively refining diverse rationales into a unified coherent pattern",
      "attributes": {
        "subtype": "aggregation"
      }
    },
    {
      "id": "df-hierarchical-filtering",
      "name": "Hierarchical Constraint Filtering",
      "type": "DataFlow",
      "description": "Applying layered constraints (hard → soft) to filter candidates",
      "attributes": {
        "subtype": "filtering"
      }
    },
    {
      "id": "df-dag-extraction",
      "name": "DAG Path Extraction",
      "type": "DataFlow",
      "description": "Extracting backbone paths between endpoints in directed acyclic graphs",
      "attributes": {
        "subtype": "chaining"
      }
    },
    {
      "id": "df-human-annotation",
      "name": "Human Annotation / Active Learning Loop",
      "type": "DataFlow",
      "description": "Humans annotate most uncertain examples for improved prompting",
      "attributes": {
        "subtype": "feedback"
      }
    },
    {
      "id": "df-query-paraphrasing",
      "name": "Paraphrasing / Crowd-Sourced Query Variants",
      "type": "DataFlow",
      "description": "Using synonymous reformulations of the query for diversity",
      "attributes": {
        "subtype": "decomposition"
      }
    },
    {
      "id": "df-demo-library",
      "name": "Cross-Task Demonstration Library",
      "type": "DataFlow",
      "description": "Maintaining a library of demos from related tasks for transfer",
      "attributes": {
        "subtype": "storage"
      }
    },
    {
      "id": "df-representative-selection",
      "name": "Representative Example Selection",
      "type": "DataFlow",
      "description": "Choosing representative examples (from clusters or pools) as demonstrations",
      "attributes": {
        "subtype": "filtering"
      }
    },
    {
      "id": "df-saliency-analysis",
      "name": "Saliency / Attention Flow Analysis",
      "type": "DataFlow",
      "description": "Analyzing information flow through attention layers to select prompts",
      "attributes": {
        "subtype": "filtering"
      }
    },
    {
      "id": "df-parallel-decomposition",
      "name": "Parallel Decomposition",
      "type": "DataFlow",
      "description": "Decomposing a problem into independent parts and processing them in parallel",
      "attributes": {
        "subtype": "decomposition"
      }
    },
    {
      "id": "df-feedback-loop",
      "name": "Feedback Loop",
      "type": "DataFlow",
      "description": "Iterative cycle of generation, evaluation, and refinement using self-generated feedback",
      "attributes": {
        "subtype": "feedback"
      }
    },
    {
      "id": "cap-arithmetic",
      "name": "Arithmetic Computation",
      "type": "CognitiveCapability",
      "description": "Performing numerical calculations, basic math operations",
      "attributes": {}
    },
    {
      "id": "cap-symbolic",
      "name": "Symbolic Reasoning",
      "type": "CognitiveCapability",
      "description": "Manipulating abstract symbols, variables, and formal expressions",
      "attributes": {}
    },
    {
      "id": "cap-spatial",
      "name": "Spatial Reasoning",
      "type": "CognitiveCapability",
      "description": "Understanding physical/spatial relationships, navigation, object positioning",
      "attributes": {}
    },
    {
      "id": "cap-temporal",
      "name": "Temporal Reasoning",
      "type": "CognitiveCapability",
      "description": "Understanding time sequences, temporal constraints, chronological ordering",
      "attributes": {}
    },
    {
      "id": "cap-deductive",
      "name": "Deductive Logic",
      "type": "CognitiveCapability",
      "description": "Drawing necessary conclusions from premises via logical rules",
      "attributes": {}
    },
    {
      "id": "cap-inductive",
      "name": "Inductive Reasoning",
      "type": "CognitiveCapability",
      "description": "Generalizing from specific examples or observations",
      "attributes": {}
    },
    {
      "id": "cap-causal",
      "name": "Causal Inference",
      "type": "CognitiveCapability",
      "description": "Understanding cause-and-effect relationships and mechanisms",
      "attributes": {}
    },
    {
      "id": "cap-analogical",
      "name": "Analogical Thinking",
      "type": "CognitiveCapability",
      "description": "Reasoning by analogy, transferring knowledge from similar domains",
      "attributes": {}
    },
    {
      "id": "cap-planning",
      "name": "Multi-Step Planning",
      "type": "CognitiveCapability",
      "description": "Formulating and executing multi-step goal-directed strategies",
      "attributes": {}
    },
    {
      "id": "cap-abstraction",
      "name": "Abstraction",
      "type": "CognitiveCapability",
      "description": "Extracting high-level concepts or principles from specific instances",
      "attributes": {}
    },
    {
      "id": "cap-self-monitoring",
      "name": "Self-Monitoring / Metacognition",
      "type": "CognitiveCapability",
      "description": "Evaluating own reasoning quality, detecting errors or uncertainties",
      "attributes": {}
    },
    {
      "id": "cap-compositional",
      "name": "Compositional Generalization",
      "type": "CognitiveCapability",
      "description": "Combining learned patterns in novel ways to solve new problems",
      "attributes": {}
    },
    {
      "id": "cap-multi-hop",
      "name": "Multi-Hop Inference",
      "type": "CognitiveCapability",
      "description": "Chaining multiple reasoning steps over different information sources",
      "attributes": {}
    },
    {
      "id": "cap-retrieval-integration",
      "name": "Knowledge Integration",
      "type": "CognitiveCapability",
      "description": "Incorporating and synthesizing information from external knowledge sources",
      "attributes": {}
    },
    {
      "id": "cap-code-synthesis",
      "name": "Code Synthesis",
      "type": "CognitiveCapability",
      "description": "Generating executable code from natural language specifications",
      "attributes": {}
    },
    {
      "id": "cap-creative-generation",
      "name": "Creative Generation",
      "type": "CognitiveCapability",
      "description": "Producing novel, creative text: stories, poetry, open-ended writing",
      "attributes": {}
    },
    {
      "id": "cap-emotional-understanding",
      "name": "Emotional Understanding",
      "type": "CognitiveCapability",
      "description": "Comprehending and responding to emotional/affective cues",
      "attributes": {}
    },
    {
      "id": "math-word-problems",
      "name": "Math Word Problems (GSM8K, SVAMP)",
      "type": "Task",
      "description": "",
      "attributes": {}
    },
    {
      "id": "math-algebraic",
      "name": "Algebraic Math (AQuA, MATH)",
      "type": "Task",
      "description": "",
      "attributes": {}
    },
    {
      "id": "math-tabular",
      "name": "Tabular Math (FinQA, TabMWP)",
      "type": "Task",
      "description": "",
      "attributes": {}
    },
    {
      "id": "math-search-based",
      "name": "Search-based Math (Game of 24)",
      "type": "Task",
      "description": "",
      "attributes": {}
    },
    {
      "id": "logical-reasoning",
      "name": "Logical Reasoning",
      "type": "Task",
      "description": "",
      "attributes": {}
    },
    {
      "id": "commonsense-reasoning",
      "name": "Commonsense Reasoning",
      "type": "Task",
      "description": "",
      "attributes": {}
    },
    {
      "id": "multi-hop-reasoning",
      "name": "Multi-Hop Reasoning",
      "type": "Task",
      "description": "",
      "attributes": {}
    },
    {
      "id": "cf-qa",
      "name": "Context-Free Question Answering",
      "type": "Task",
      "description": "",
      "attributes": {}
    },
    {
      "id": "contextual-qa",
      "name": "Contextual Question Answering",
      "type": "Task",
      "description": "",
      "attributes": {}
    },
    {
      "id": "conversational-qa",
      "name": "Conversational Contextual QA",
      "type": "Task",
      "description": "",
      "attributes": {}
    },
    {
      "id": "spatial-qa",
      "name": "Spatial Question Answering",
      "type": "Task",
      "description": "",
      "attributes": {}
    },
    {
      "id": "table-math",
      "name": "Table-Based Math Problem Solving",
      "type": "Task",
      "description": "",
      "attributes": {}
    },
    {
      "id": "table-qa",
      "name": "Table-Based Question Answering",
      "type": "Task",
      "description": "",
      "attributes": {}
    },
    {
      "id": "table-truth",
      "name": "Table-Based Truthfulness",
      "type": "Task",
      "description": "",
      "attributes": {}
    },
    {
      "id": "truthfulness",
      "name": "Truthfulness / Factual Accuracy",
      "type": "Task",
      "description": "",
      "attributes": {}
    },
    {
      "id": "free-response",
      "name": "Free Response Generation",
      "type": "Task",
      "description": "",
      "attributes": {}
    },
    {
      "id": "code-gen",
      "name": "Code Generation",
      "type": "Task",
      "description": "",
      "attributes": {}
    },
    {
      "id": "dialogue",
      "name": "Dialogue Systems",
      "type": "Task",
      "description": "",
      "attributes": {}
    },
    {
      "id": "social-reasoning",
      "name": "Social Reasoning",
      "type": "Task",
      "description": "",
      "attributes": {}
    },
    {
      "id": "causal-reasoning",
      "name": "Causal Reasoning",
      "type": "Task",
      "description": "",
      "attributes": {}
    },
    {
      "id": "text-classification",
      "name": "Multilabel Text Classification",
      "type": "Task",
      "description": "",
      "attributes": {}
    },
    {
      "id": "task-completion",
      "name": "Language-Based Task Completion",
      "type": "Task",
      "description": "",
      "attributes": {}
    },
    {
      "id": "relation-extraction",
      "name": "Relation Extraction",
      "type": "Task",
      "description": "",
      "attributes": {}
    },
    {
      "id": "nli",
      "name": "Natural Language Inference",
      "type": "Task",
      "description": "",
      "attributes": {}
    },
    {
      "id": "stance-detection",
      "name": "Stance Detection",
      "type": "Task",
      "description": "",
      "attributes": {}
    },
    {
      "id": "paraphrasing",
      "name": "Paraphrasing",
      "type": "Task",
      "description": "",
      "attributes": {}
    },
    {
      "id": "summarization",
      "name": "Summarization",
      "type": "Task",
      "description": "",
      "attributes": {}
    },
    {
      "id": "wsd",
      "name": "Word Sense Disambiguation",
      "type": "Task",
      "description": "",
      "attributes": {}
    },
    {
      "id": "ner",
      "name": "Named Entity Recognition",
      "type": "Task",
      "description": "",
      "attributes": {}
    },
    {
      "id": "machine-translation",
      "name": "Machine Translation",
      "type": "Task",
      "description": "",
      "attributes": {}
    },
    {
      "id": "sentiment",
      "name": "Emotion / Sentiment Understanding",
      "type": "Task",
      "description": "",
      "attributes": {}
    },
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        "requires_large_model": true,
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "binder",
      "name": "Binder",
      "description": "",
      "members": [
        "alg-code-execution",
        "cap-code-synthesis",
        "df-tabular-reasoning",
        "pc-code-generation",
        "table-qa",
        "table-truth"
      ],
      "attributes": {
        "input": "Query → parsed to program binding LLM API as function → executed",
        "output": "Program output (neural-symbolic)",
        "cost": "moderate",
        "llm_calls": "1 + code exec",
        "source_papers": [
          "Survey of Prompt Engineering Methods - Vatsal et al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "bot",
      "name": "Buffer of Thoughts (BoT)",
      "description": "",
      "members": [
        "alg-thought-evaluation",
        "br-bot-cost",
        "commonsense-reasoning",
        "df-external-memory",
        "logical-reasoning",
        "math-search-based",
        "pc-template-reuse"
      ],
      "attributes": {
        "input": "Query → retrieve thought-template → instantiate reasoning",
        "output": "Template-guided reasoning → answer",
        "cost": "moderate",
        "llm_calls": 2,
        "note": "12% of ToT computational cost",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "ccot",
      "name": "Contrastive Chain-of-Thought (CCoT)",
      "description": "",
      "members": [
        "br-ccot-bamboogle",
        "br-ccot-gsm8k",
        "math-algebraic",
        "math-word-problems",
        "multi-hop-reasoning",
        "pc-contrastive-examples",
        "pc-intermediate-reasoning"
      ],
      "attributes": {
        "input": "Query + valid AND invalid reasoning demonstrations",
        "output": "Improved reasoning trace → answer",
        "cost": "low",
        "llm_calls": 1,
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "cd-cot",
      "name": "Contrastive Denoising with Noisy CoT (CD-CoT)",
      "description": "",
      "members": [
        "alg-majority-voting",
        "alg-multiple-sampling",
        "commonsense-reasoning",
        "math-algebraic",
        "math-word-problems",
        "pc-contrastive-examples"
      ],
      "attributes": {
        "input": "Query + diverse noisy CoT samples",
        "output": "Denoised majority answer",
        "cost": "high",
        "llm_calls": "N samples",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "chain-of-table",
      "name": "Chain of Table Prompting",
      "description": "",
      "members": [
        "df-tabular-reasoning",
        "pc-intermediate-reasoning",
        "table-qa",
        "table-truth"
      ],
      "attributes": {
        "input": "Table + query → iterative table operations (add_col, sort, filter) → answer",
        "output": "Evolved table representation → answer",
        "cost": "high",
        "llm_calls": "iterative (plan + execute per step)",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "chameleon",
      "name": "Chameleon",
      "description": "",
      "members": [
        "alg-tool-use",
        "cap-planning",
        "contextual-qa",
        "pc-role-assignment",
        "task-completion"
      ],
      "attributes": {
        "input": "Query → LLM plans tool sequence → orchestrates execution",
        "output": "Multi-tool composed answer",
        "cost": "very_high",
        "llm_calls": "planner + tools",
        "source_papers": [
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "coc",
      "name": "Chain of Code (CoC) Prompting",
      "description": "",
      "members": [
        "alg-code-execution",
        "alg-lmulator",
        "br-coc-bbh",
        "cap-arithmetic",
        "cap-causal",
        "cap-code-synthesis",
        "causal-reasoning",
        "commonsense-reasoning",
        "logical-reasoning",
        "machine-translation",
        "math-algebraic",
        "math-tabular",
        "pc-code-generation",
        "pc-intermediate-reasoning",
        "recommender",
        "sentiment",
        "spatial-qa",
        "table-math"
      ],
      "attributes": {
        "input": "Query → LLM generates code + LMulator simulates non-executable parts",
        "output": "Interleaved execution + simulation → answer",
        "cost": "moderate",
        "llm_calls": "2+ (gen + simulate)",
        "note": "84% on BIG-Bench Hard (+12%)",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "cod",
      "name": "Chain of Draft (CoD)",
      "description": "",
      "members": [
        "br-cod-tokens",
        "cap-arithmetic",
        "commonsense-reasoning",
        "math-algebraic",
        "math-word-problems",
        "pc-concise-reasoning",
        "pc-intermediate-reasoning"
      ],
      "attributes": {
        "input": "Query + instruction for minimal-draft reasoning",
        "output": "Concise reasoning tokens → answer",
        "cost": "minimal",
        "llm_calls": 1,
        "token_savings": "80% fewer tokens than CoT",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "code-prompting",
      "name": "Code Prompting",
      "description": "",
      "members": [
        "code-gen",
        "logical-reasoning",
        "pc-code-generation"
      ],
      "attributes": {
        "input": "Query → LLM generates code representation of conditional logic",
        "output": "Code-structured reasoning → answer",
        "cost": "low",
        "llm_calls": 1,
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "coe",
      "name": "Chain-of-Event (CoE)",
      "description": "",
      "members": [
        "df-event-extraction",
        "summarization"
      ],
      "attributes": {
        "input": "Document → event extraction → generalization → filtering → chronological integration",
        "output": "Concise event-based summary",
        "cost": "moderate",
        "llm_calls": 4,
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "cok",
      "name": "Chain-of-Knowledge (CoK) Prompting",
      "description": "",
      "members": [
        "alg-retrieval",
        "cap-multi-hop",
        "cap-retrieval-integration",
        "cf-qa",
        "df-context-augmentation",
        "df-knowledge-adaptation",
        "hallucination-reduction",
        "multi-hop-reasoning",
        "table-qa",
        "truthfulness"
      ],
      "attributes": {
        "input": "Query → reasoning prep → dynamic knowledge adaptation from heterogeneous sources",
        "output": "Knowledge-corrected rationale → answer",
        "cost": "high",
        "llm_calls": "3+ (prep, adapt, consolidate)",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "complex-cot",
      "name": "Complex CoT",
      "description": "",
      "members": [
        "alg-majority-voting",
        "alg-multiple-sampling",
        "commonsense-reasoning",
        "math-algebraic",
        "math-word-problems",
        "multi-hop-reasoning",
        "pc-complexity-selection",
        "pc-intermediate-reasoning",
        "table-math"
      ],
      "attributes": {
        "input": "Query + longest/most-complex available exemplars",
        "output": "N sampled complex chains → top-K voted answer",
        "cost": "high",
        "llm_calls": "N samples",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "con",
      "name": "Chain-of-Note (CoN) Prompting",
      "description": "",
      "members": [
        "alg-retrieval",
        "br-con-noisy",
        "cap-retrieval-integration",
        "df-context-augmentation",
        "df-document-scoring",
        "hallucination-reduction"
      ],
      "attributes": {
        "input": "Query + retrieved docs → systematic note-taking scoring relevance",
        "output": "Relevance-filtered grounded answer",
        "cost": "moderate",
        "llm_calls": 2,
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "contrastive-sc",
      "name": "Contrastive Self-Consistency",
      "description": "",
      "members": [
        "alg-majority-voting",
        "math-algebraic",
        "math-word-problems",
        "multi-hop-reasoning",
        "pc-contrastive-examples"
      ],
      "attributes": {
        "input": "Contrastive CoT prompt → N samples",
        "output": "Majority voted answer from contrastive samples",
        "cost": "high",
        "llm_calls": "N samples",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "coop",
      "name": "Context Optimization (CoOp)",
      "description": "Added from post-migration paper pass over all raw papers.",
      "members": [
        "cap-compositional",
        "pc-task-instruction",
        "task-completion"
      ],
      "attributes": {
        "source_papers": [
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt"
        ],
        "provisional": true
      }
    },
    {
      "id": "cos",
      "name": "Chain-of-Symbol (CoS) Prompting",
      "description": "",
      "members": [
        "br-cos-spatial-vatsal",
        "cap-spatial",
        "cap-symbolic",
        "pc-symbolic-representation",
        "spatial-qa"
      ],
      "attributes": {
        "input": "Query + few-shot with symbolic spatial representations",
        "output": "Symbolic reasoning chain → answer",
        "cost": "low",
        "llm_calls": 1,
        "token_savings": "More compact than NL CoT",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "cot",
      "name": "Chain-of-Thought (CoT) Prompting",
      "description": "",
      "members": [
        "br-cot-commonsense-vatsal",
        "br-cot-gsm8k",
        "br-cot-math-vatsal",
        "cap-arithmetic",
        "causal-reasoning",
        "cf-qa",
        "code-gen",
        "commonsense-reasoning",
        "contextual-qa",
        "conversational-qa",
        "dialogue",
        "free-response",
        "logical-reasoning",
        "machine-translation",
        "math-algebraic",
        "math-word-problems",
        "multi-hop-reasoning",
        "ner",
        "nli",
        "paraphrasing",
        "pc-intermediate-reasoning",
        "recommender",
        "relation-extraction",
        "sentiment",
        "social-reasoning",
        "spatial-qa",
        "stance-detection",
        "table-math",
        "table-qa",
        "table-truth",
        "task-completion",
        "text-classification",
        "truthfulness",
        "wsd"
      ],
      "attributes": {
        "input": "Query + exemplars with reasoning steps",
        "output": "Reasoning trace → final answer",
        "cost": "low",
        "llm_calls": 1,
        "requires_large_model": true,
        "note": "Emergent at ≥100B params (PaLM-540B)",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": [
          "math-tabular"
        ]
      }
    },
    {
      "id": "cove",
      "name": "Chain-of-Verification (CoVe)",
      "description": "",
      "members": [
        "br-cove-qa",
        "cap-self-monitoring",
        "cf-qa",
        "contextual-qa",
        "df-verification",
        "free-response",
        "hallucination-reduction",
        "pc-intermediate-reasoning"
      ],
      "attributes": {
        "input": "Query → draft response → verification Qs → verified revision",
        "output": "Hallucination-reduced answer",
        "cost": "high",
        "llm_calls": "4+ (generate, plan, verify, revise)",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "critic",
      "name": "CRITIC Framework",
      "description": "",
      "members": [
        "alg-tool-use",
        "cap-self-monitoring",
        "code-gen",
        "df-iterative-refinement",
        "df-self-reflection",
        "free-response",
        "hallucination-reduction"
      ],
      "attributes": {
        "input": "Query → draft → external tools evaluate → LLM refines → iterate",
        "output": "Externally validated refined answer",
        "cost": "high",
        "llm_calls": "iterative + tool calls",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "cumulative-reasoning",
      "name": "Cumulative Reasoning",
      "description": "",
      "members": [
        "alg-majority-voting",
        "alg-thought-evaluation",
        "cap-arithmetic",
        "cap-deductive",
        "df-iterative-refinement",
        "df-verification",
        "logical-reasoning",
        "math-algebraic",
        "math-word-problems",
        "pc-intermediate-reasoning"
      ],
      "attributes": {
        "input": "Query → proposer suggests steps → verifier validates → accumulate → reporter synthesizes",
        "output": "DAG of verified propositions → final answer",
        "cost": "very_high",
        "llm_calls": "many (propose → verify → report cycles)",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "dater",
      "name": "DATER",
      "description": "",
      "members": [
        "alg-code-execution",
        "df-decomposition",
        "df-tabular-reasoning",
        "table-qa",
        "table-truth"
      ],
      "attributes": {
        "input": "Large table → decompose to sub-tables → SQL sub-queries → few-shot reasoning",
        "output": "Sub-table + sub-query answers → final answer",
        "cost": "high",
        "llm_calls": "3+ + SQL exec",
        "source_papers": [
          "Survey of Prompt Engineering Methods - Vatsal et al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "decomp",
      "name": "Decomposed Prompting (DecomP)",
      "description": "",
      "members": [
        "br-decomp-commonsense",
        "cap-compositional",
        "cap-multi-hop",
        "cap-planning",
        "commonsense-reasoning",
        "df-decomposition",
        "df-sequential-chaining",
        "multi-hop-reasoning"
      ],
      "attributes": {
        "input": "Query → hierarchical/recursive decomposition → sub-LM solvers",
        "output": "Aggregated sub-answers → final answer",
        "cost": "high",
        "llm_calls": "1 + N sub-solvers + tools",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "detox-chain",
      "name": "Detox-Chain",
      "description": "",
      "members": [
        "df-iterative-refinement",
        "dialogue",
        "free-response",
        "pc-context-filtering"
      ],
      "attributes": {
        "input": "Toxic text → identify toxic elements → substitute → generate safe output",
        "output": "Detoxified prompt/response",
        "cost": "moderate",
        "llm_calls": "2-3",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "diverse",
      "name": "DiVeRSe",
      "description": "",
      "members": [
        "alg-majority-voting",
        "alg-multiple-sampling",
        "commonsense-reasoning",
        "df-verification",
        "math-algebraic",
        "math-word-problems"
      ],
      "attributes": {
        "input": "Multiple diverse prompts → completions → step-aware verifier → vote",
        "output": "Step-verified voted answer",
        "cost": "very_high",
        "llm_calls": "K prompts × N samples + verifier",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "echo",
      "name": "Self-Harmonized CoT (ECHO)",
      "description": "",
      "members": [
        "alg-clustering",
        "commonsense-reasoning",
        "df-reasoning-harmonization",
        "df-representative-selection",
        "math-algebraic",
        "math-word-problems",
        "multi-hop-reasoning"
      ],
      "attributes": {
        "input": "Question pool → clustered → diverse rationales unified",
        "output": "Harmonized reasoning → answer",
        "cost": "moderate",
        "llm_calls": "K+2",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "eedp",
      "name": "End-to-End DAG-Path (EEDP) Prompting",
      "description": "",
      "members": [
        "contextual-qa",
        "df-dag-extraction",
        "df-graph-reasoning",
        "multi-hop-reasoning"
      ],
      "attributes": {
        "input": "Entity-linked DAG → backbone path extraction",
        "output": "Flattened path input → answer",
        "cost": "moderate",
        "llm_calls": 1,
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "emotion-prompting",
      "name": "Emotion Prompting",
      "description": "",
      "members": [
        "br-emotion-bigbench",
        "br-emotion-induction",
        "cap-creative-generation",
        "cap-emotional-understanding",
        "dialogue",
        "free-response",
        "pc-emotional-stimulus",
        "pc-task-instruction",
        "sentiment"
      ],
      "attributes": {
        "input": "Query + emotional stimulus sentences appended",
        "output": "Emotionally enhanced response",
        "cost": "minimal",
        "llm_calls": 1,
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "ensemble-refinement",
      "name": "Ensemble Refinement (ER)",
      "description": "",
      "members": [
        "alg-majority-voting",
        "alg-multiple-sampling",
        "cf-qa",
        "df-iterative-refinement"
      ],
      "attributes": {
        "input": "CoT prompt → N generations → concatenate → M refinements → vote",
        "output": "Refined majority-voted answer",
        "cost": "high",
        "llm_calls": "N + M + voting",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "faithful-cot",
      "name": "Faithful CoT",
      "description": "",
      "members": [
        "alg-code-execution",
        "cap-symbolic",
        "logical-reasoning",
        "math-algebraic",
        "math-word-problems",
        "pc-code-generation"
      ],
      "attributes": {
        "input": "Query → NL decomposition → symbolic program (Python/Datalog)",
        "output": "Interpretable symbolic reasoning → executed answer",
        "cost": "moderate",
        "llm_calls": "1 + code exec",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "fed-sp-dp",
      "name": "Federated SP/DP Self-Consistency/CoT",
      "description": "",
      "members": [
        "alg-majority-voting",
        "alg-multiple-sampling",
        "df-query-paraphrasing",
        "math-algebraic",
        "math-word-problems"
      ],
      "attributes": {
        "input": "Query paraphrased (same/diff params) → answered → SC/CoT federated",
        "output": "Majority-voted or hint-guided answer",
        "cost": "high",
        "llm_calls": "K paraphrases + K answers + voting",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "few-shot",
      "name": "Few-Shot Prompting",
      "description": "",
      "members": [
        "cap-analogical",
        "cap-inductive",
        "cf-qa",
        "code-gen",
        "contextual-qa",
        "free-response",
        "machine-translation",
        "ner",
        "nli",
        "pc-task-instruction",
        "sentiment",
        "text-classification"
      ],
      "attributes": {
        "input": "Query + demonstration examples",
        "output": "Answer following demonstrated pattern",
        "cost": "low",
        "llm_calls": 1,
        "requires_large_model": false,
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "generated-knowledge",
      "name": "Generated Knowledge Prompting",
      "description": "Added from post-migration paper pass over all raw papers.",
      "members": [
        "cap-retrieval-integration",
        "contextual-qa",
        "df-context-augmentation"
      ],
      "attributes": {
        "source_papers": [
          "Survey of Prompt Engineering Methods - Vatsal et al.txt"
        ],
        "provisional": true
      }
    },
    {
      "id": "gnn-rag",
      "name": "GNN-RAG",
      "description": "",
      "members": [
        "alg-kg-reasoning",
        "alg-retrieval",
        "contextual-qa",
        "multi-hop-reasoning"
      ],
      "attributes": {
        "input": "Query → GNN reasons over KG → LLM generates from subgraph",
        "output": "GNN-extracted answer candidates → NL answer",
        "cost": "high",
        "llm_calls": "1 + GNN inference",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "gorilla",
      "name": "Gorilla",
      "description": "",
      "members": [
        "alg-tool-use",
        "cap-code-synthesis",
        "code-gen",
        "task-completion"
      ],
      "attributes": {
        "input": "Query → LLM selects/generates correct API call from large catalog",
        "output": "Correct API invocation + result",
        "cost": "moderate",
        "llm_calls": 1,
        "source_papers": [
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "got",
      "name": "Graph-of-Thought (GoT) Prompting",
      "description": "",
      "members": [
        "alg-backtracking",
        "alg-thought-evaluation",
        "cap-planning",
        "df-graph-reasoning",
        "free-response",
        "logical-reasoning",
        "math-search-based"
      ],
      "attributes": {
        "input": "Query → DAG of interconnected thoughts",
        "output": "Synthesized solution from graph traversal",
        "cost": "very_high",
        "llm_calls": "many",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "graph-rag",
      "name": "GraphRAG",
      "description": "",
      "members": [
        "alg-kg-reasoning",
        "alg-retrieval",
        "cap-retrieval-integration",
        "contextual-qa",
        "hallucination-reduction",
        "multi-hop-reasoning"
      ],
      "attributes": {
        "input": "Query → KG-based retrieval + community summarization",
        "output": "KG-grounded answer",
        "cost": "high",
        "llm_calls": "1 + KG traversal",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "hag",
      "name": "HAG (Hyperparameter-Aware Generation)",
      "description": "",
      "members": [
        "cap-self-monitoring",
        "df-iterative-refinement",
        "free-response",
        "text-classification"
      ],
      "attributes": {
        "input": "Query → LLM generates optimal decoding hyperparameters → generates with them",
        "output": "Self-configured optimally decoded response",
        "cost": "moderate",
        "llm_calls": 2,
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "hugginggpt",
      "name": "HuggingGPT",
      "description": "",
      "members": [
        "alg-tool-use",
        "cap-planning",
        "free-response",
        "pc-role-assignment",
        "task-completion"
      ],
      "attributes": {
        "input": "Query → task planning → model selection → execution → response integration",
        "output": "Multi-model orchestrated answer",
        "cost": "very_high",
        "llm_calls": "planner + N model calls",
        "source_papers": [
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "hyde",
      "name": "HyDE (Hypothetical Document Embeddings)",
      "description": "",
      "members": [
        "alg-retrieval",
        "contextual-qa",
        "multi-hop-reasoning"
      ],
      "attributes": {
        "input": "Query → LLM generates hypothetical doc → used as retrieval query",
        "output": "Better-aligned retrieved documents",
        "cost": "moderate",
        "llm_calls": "1 + retrieval",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "iap",
      "name": "Instance-Adaptive Prompting (IAP)",
      "description": "",
      "members": [
        "alg-uncertainty-estimation",
        "commonsense-reasoning",
        "df-saliency-analysis",
        "math-algebraic",
        "math-word-problems"
      ],
      "attributes": {
        "input": "Query → saliency/uncertainty scoring → adapted prompt",
        "output": "Instance-tuned reasoning → answer",
        "cost": "moderate",
        "llm_calls": 2,
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "instructed-prompting",
      "name": "Instructed Prompting",
      "description": "",
      "members": [
        "br-instructed-truth",
        "pc-context-filtering",
        "truthfulness"
      ],
      "attributes": {
        "input": "Query + explicit instruction to ignore irrelevant info",
        "output": "Direct answer (distraction-free)",
        "cost": "minimal",
        "llm_calls": 1,
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "least-to-most",
      "name": "Least-to-Most Prompting",
      "description": "",
      "members": [
        "cap-compositional",
        "cap-planning",
        "commonsense-reasoning",
        "contextual-qa",
        "df-decomposition",
        "df-sequential-chaining",
        "math-algebraic",
        "math-word-problems",
        "multi-hop-reasoning",
        "task-completion",
        "truthfulness"
      ],
      "attributes": {
        "input": "Query → decomposition prompt → sub-Qs solved sequentially",
        "output": "Sequential sub-answers → final answer",
        "cost": "moderate",
        "llm_calls": "1 + N sub-problems",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "logic-of-thought",
      "name": "Logic-of-Thought Prompting",
      "description": "",
      "members": [
        "cap-deductive",
        "cap-symbolic",
        "logical-reasoning",
        "math-algebraic",
        "math-word-problems",
        "pc-intermediate-reasoning",
        "pc-propositional-logic"
      ],
      "attributes": {
        "input": "Query → extract propositions → apply logic laws → augmented prompt",
        "output": "Logic-augmented reasoning → answer",
        "cost": "moderate",
        "llm_calls": 2,
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "logicot",
      "name": "Logical CoT (LogiCoT) Prompting",
      "description": "",
      "members": [
        "br-lot-causal",
        "br-lot-commonsense",
        "cap-causal",
        "cap-deductive",
        "cap-self-monitoring",
        "causal-reasoning",
        "df-reductio-ad-absurdum",
        "df-verification",
        "logical-reasoning",
        "math-algebraic",
        "math-word-problems",
        "pc-intermediate-reasoning",
        "pc-propositional-logic",
        "social-reasoning"
      ],
      "attributes": {
        "input": "Query + reductio-ad-absurdum verification loop",
        "output": "Verified reasoning trace → answer",
        "cost": "high",
        "llm_calls": "iterative",
        "requires_large_model": true,
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "lot-layer",
      "name": "Layer-of-Thoughts (LoT)",
      "description": "",
      "members": [
        "commonsense-reasoning",
        "df-hierarchical-filtering",
        "logical-reasoning"
      ],
      "attributes": {
        "input": "Query + hierarchical constraint layers (hard → soft)",
        "output": "Filtered candidates → answer",
        "cost": "moderate",
        "llm_calls": "layers",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "maieutic",
      "name": "Maieutic Prompting",
      "description": "",
      "members": [
        "alg-tree-search",
        "br-maieutic-commonsense",
        "cap-deductive",
        "commonsense-reasoning",
        "df-reductio-ad-absurdum"
      ],
      "attributes": {
        "input": "Query → recursive hypothesis generation → tree of propositions → belief scoring",
        "output": "Contradiction-free answer",
        "cost": "very_high",
        "llm_calls": "recursive (many)",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "mathprompter",
      "name": "MathPrompter",
      "description": "",
      "members": [
        "alg-code-execution",
        "br-mathprompter-math",
        "cap-arithmetic",
        "df-verification",
        "math-algebraic",
        "math-tabular",
        "pc-algebraic-abstraction"
      ],
      "attributes": {
        "input": "Query → algebraic expression with variables → Python function → multi-run validation",
        "output": "Verified numerical answer",
        "cost": "moderate",
        "llm_calls": "2 + N validations + code exec",
        "source_papers": [
          "Survey of Prompt Engineering Methods - Vatsal et al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "meta-rag",
      "name": "MetaRAG",
      "description": "",
      "members": [
        "alg-retrieval",
        "br-metarag-2wiki",
        "br-metarag-hotpot",
        "cap-multi-hop",
        "multi-hop-reasoning"
      ],
      "attributes": {
        "input": "Retrieved info + metacognitive monitoring/evaluation/planning",
        "output": "Metacognitively refined answer",
        "cost": "high",
        "llm_calls": "3+ (monitor + evaluate + plan)",
        "source_papers": [
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "metacognitive",
      "name": "Metacognitive Prompting (MP)",
      "description": "",
      "members": [
        "br-mp-various",
        "cap-abstraction",
        "cap-self-monitoring",
        "contextual-qa",
        "df-self-reflection",
        "ner",
        "nli",
        "paraphrasing",
        "pc-intermediate-reasoning",
        "pc-role-assignment",
        "relation-extraction",
        "text-classification",
        "wsd"
      ],
      "attributes": {
        "input": "Query → 5 stages: understand → judge → evaluate → decide → confidence",
        "output": "Multi-stage self-reflective answer + confidence",
        "cost": "moderate",
        "llm_calls": "1 (structured) or 5",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "minds-eye",
      "name": "Mind's Eye",
      "description": "",
      "members": [
        "alg-physics-simulation",
        "cap-spatial",
        "physical-reasoning",
        "spatial-qa"
      ],
      "attributes": {
        "input": "Physical question → text-to-code → MuJoCo simulation → NL answer",
        "output": "Physically grounded answer",
        "cost": "high",
        "llm_calls": "2 + physics sim",
        "requires_external": "MuJoCo physics engine",
        "source_papers": [],
        "fails_at": []
      }
    },
    {
      "id": "mrkl",
      "name": "MRKL System",
      "description": "",
      "members": [
        "alg-tool-use",
        "cap-planning",
        "cf-qa",
        "task-completion"
      ],
      "attributes": {
        "input": "Query → routed to specialized module APIs → integrated result",
        "output": "Multi-module integrated answer",
        "cost": "high",
        "llm_calls": "1 router + K module calls",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "multi-agent-debate",
      "name": "Multi-Agent Debate (MAD)",
      "description": "",
      "members": [
        "alg-majority-voting",
        "alg-multiple-sampling",
        "cap-deductive",
        "cap-self-monitoring",
        "df-feedback-loop",
        "df-iterative-refinement",
        "hallucination-reduction",
        "logical-reasoning",
        "math-algebraic",
        "math-word-problems",
        "pc-role-assignment",
        "truthfulness"
      ],
      "attributes": {
        "input": "Query → N agents debate iteratively → judge selects best answer",
        "output": "Consensus answer from multi-agent deliberation",
        "cost": "very_high",
        "llm_calls": "N agents × K rounds + judge",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "multibot",
      "name": "MultiPoT",
      "description": "",
      "members": [
        "alg-code-execution",
        "alg-majority-voting",
        "code-gen",
        "math-algebraic",
        "math-word-problems"
      ],
      "attributes": {
        "input": "Query → solutions in multiple PLs → SC selects best",
        "output": "Best cross-PL executed answer",
        "cost": "high",
        "llm_calls": "K languages + code exec + voting",
        "source_papers": [
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "not",
      "name": "Narrative-of-Thought (NoT) Prompting",
      "description": "",
      "members": [
        "cap-temporal",
        "contextual-qa",
        "dialogue",
        "pc-code-generation",
        "pc-narrative-construction",
        "summarization"
      ],
      "attributes": {
        "input": "Query → temporally ordered narrative construction",
        "output": "Grounded narrative → answer",
        "cost": "low",
        "llm_calls": 1,
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "opro",
      "name": "Optimization by Prompting (OPRO)",
      "description": "",
      "members": [
        "alg-auto-prompt-gen",
        "br-opro-gsm8k",
        "df-iterative-refinement",
        "math-algebraic",
        "math-word-problems",
        "text-classification"
      ],
      "attributes": {
        "input": "Meta-prompt with optimization trajectory (solution,score pairs) → LLM proposes new solutions",
        "output": "Optimized prompt / solution",
        "cost": "very_high",
        "llm_calls": "iterative (many rounds)",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "pal",
      "name": "Program-Aided Language Models (PAL)",
      "description": "",
      "members": [
        "alg-code-execution",
        "cap-arithmetic",
        "cap-code-synthesis",
        "commonsense-reasoning",
        "conversational-qa",
        "logical-reasoning",
        "math-algebraic",
        "math-tabular",
        "pc-code-generation",
        "table-math"
      ],
      "attributes": {
        "input": "Query → LLM generates interleaved NL + Python code",
        "output": "Code execution → answer",
        "cost": "moderate",
        "llm_calls": "1 + code exec",
        "requires_external": "Python interpreter",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "plan-and-solve",
      "name": "Plan-and-Solve (PS) Prompting",
      "description": "",
      "members": [
        "cap-planning",
        "commonsense-reasoning",
        "contextual-qa",
        "df-decomposition",
        "math-algebraic",
        "math-word-problems",
        "multi-hop-reasoning",
        "ner",
        "nli",
        "paraphrasing",
        "pc-intermediate-reasoning",
        "pc-task-instruction",
        "relation-extraction",
        "text-classification",
        "wsd"
      ],
      "attributes": {
        "input": "Query + 'Let's first understand and devise a plan…'",
        "output": "Plan → step-by-step solution → answer",
        "cost": "low",
        "llm_calls": 1,
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "pot",
      "name": "Program of Thoughts (PoT) Prompting",
      "description": "",
      "members": [
        "alg-code-execution",
        "br-pot-math",
        "cap-arithmetic",
        "cap-code-synthesis",
        "cap-symbolic",
        "contextual-qa",
        "conversational-qa",
        "math-algebraic",
        "math-tabular",
        "pc-code-generation",
        "table-math"
      ],
      "attributes": {
        "input": "Query → LLM generates Python program expressing reasoning",
        "output": "Executed code output → numerical answer",
        "cost": "moderate",
        "llm_calls": "1 + code exec",
        "requires_external": "Python interpreter",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "prompt-boosting",
      "name": "PromptBoosting",
      "description": "",
      "members": [
        "alg-boosting",
        "sentiment",
        "text-classification"
      ],
      "attributes": {
        "input": "Prepared prompt set → AdaBoost-style iterative selection/weighting",
        "output": "Ensemble-boosted classification",
        "cost": "high",
        "llm_calls": "10+ per instance",
        "source_papers": [
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "query2doc",
      "name": "Query2Doc",
      "description": "",
      "members": [
        "alg-retrieval",
        "br-query2doc-msmarco",
        "br-query2doc-trecdl",
        "contextual-qa",
        "multi-hop-reasoning"
      ],
      "attributes": {
        "input": "Query → LLM generates pseudo-document → concat with query for retrieval",
        "output": "Expanded query → improved retrieval",
        "cost": "moderate",
        "llm_calls": "1 + retrieval",
        "source_papers": [
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "r-cot",
      "name": "Reverse Chain-of-Thought (R-CoT)",
      "description": "",
      "members": [
        "df-verification",
        "math-algebraic",
        "math-word-problems",
        "pc-intermediate-reasoning"
      ],
      "attributes": {
        "input": "Query → generate answer → reverse-verify by reconstructing problem",
        "output": "Verified answer (reconstruction check)",
        "cost": "moderate",
        "llm_calls": 2,
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "rag",
      "name": "Retrieval Augmented Generation (RAG)",
      "description": "",
      "members": [
        "alg-retrieval",
        "cap-retrieval-integration",
        "df-context-augmentation",
        "hallucination-reduction"
      ],
      "attributes": {
        "input": "Query → retriever fetches top-k docs → query + docs",
        "output": "Knowledge-grounded answer",
        "cost": "moderate",
        "llm_calls": "1 + retrieval",
        "requires_external": "retriever + knowledge base",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "rar",
      "name": "Rephrase and Respond (RaR)",
      "description": "",
      "members": [
        "cf-qa",
        "contextual-qa",
        "pc-question-rephrasing"
      ],
      "attributes": {
        "input": "Query + instruction to rephrase before answering",
        "output": "Rephrased question + improved answer",
        "cost": "minimal",
        "llm_calls": "1 (single-turn) or 2",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "react",
      "name": "ReAct Prompting",
      "description": "",
      "members": [
        "alg-tool-use",
        "br-react-taskcompletion",
        "cap-multi-hop",
        "cap-planning",
        "df-action-observation",
        "multi-hop-reasoning",
        "pc-intermediate-reasoning",
        "pc-role-assignment",
        "task-completion",
        "truthfulness"
      ],
      "attributes": {
        "input": "Query → interleaved Thought / Action / Observation traces",
        "output": "Action results + reasoning → final answer",
        "cost": "high",
        "llm_calls": "iterative + tool calls",
        "requires_external": "tools / envs",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "reflexion",
      "name": "Reflexion",
      "description": "",
      "members": [
        "alg-tool-use",
        "cap-planning",
        "cap-self-monitoring",
        "code-gen",
        "df-external-memory",
        "df-feedback-loop",
        "df-iterative-refinement",
        "df-self-reflection",
        "multi-hop-reasoning",
        "pc-intermediate-reasoning",
        "task-completion"
      ],
      "attributes": {
        "input": "Query → actor generates trajectory → evaluator scores → reflector critiques → memory updated → retry",
        "output": "Iteratively improved action trace → answer",
        "cost": "very_high",
        "llm_calls": "3+ per iteration (actor + evaluator + reflector)",
        "requires_external": "environment / evaluator",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "s2a",
      "name": "System 2 Attention (S2A) Prompting",
      "description": "",
      "members": [
        "br-s2a-gsmic",
        "br-s2a-triviaqa",
        "pc-context-filtering",
        "truthfulness"
      ],
      "attributes": {
        "input": "Query + context → LLM filters irrelevant context → answers from filtered",
        "output": "Attention-filtered answer",
        "cost": "moderate",
        "llm_calls": 2,
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "scot",
      "name": "Structured Chain-of-Thought (SCoT)",
      "description": "",
      "members": [
        "alg-code-execution",
        "br-scot-humaneval",
        "cap-code-synthesis",
        "code-gen",
        "pc-code-generation",
        "pc-intermediate-reasoning"
      ],
      "attributes": {
        "input": "Query → program-structured reasoning (seq/branch/loop) → code",
        "output": "Structured pseudocode reasoning → generated source code",
        "cost": "low",
        "llm_calls": 1,
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "scratchpad",
      "name": "Scratchpad Prompting",
      "description": "",
      "members": [
        "alg-code-execution",
        "br-scratchpad-mbpp",
        "code-gen",
        "df-external-memory",
        "math-algebraic",
        "math-word-problems"
      ],
      "attributes": {
        "input": "Query + instruction to emit intermediate tokens on scratchpad",
        "output": "Scratchpad trace + final answer",
        "cost": "low",
        "llm_calls": 1,
        "note": "Fixed 512-token context window",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "self-consistency",
      "name": "Self-Consistency",
      "description": "",
      "members": [
        "alg-majority-voting",
        "alg-multiple-sampling",
        "br-sc-gsm8k",
        "br-sc-math-vatsal",
        "cap-arithmetic",
        "cap-self-monitoring",
        "cf-qa",
        "commonsense-reasoning",
        "conversational-qa",
        "free-response",
        "math-algebraic",
        "math-word-problems",
        "multi-hop-reasoning",
        "nli",
        "paraphrasing",
        "pc-intermediate-reasoning",
        "relation-extraction",
        "table-math",
        "table-qa",
        "text-classification",
        "truthfulness",
        "wsd"
      ],
      "attributes": {
        "input": "CoT prompt + query (sampled N times)",
        "output": "Set[reasoning traces] → majority-voted answer",
        "cost": "high",
        "llm_calls": "N (typically 5-40)",
        "requires_large_model": true,
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": [
          "free-response"
        ]
      }
    },
    {
      "id": "self-contrast",
      "name": "Self-Contrast",
      "description": "",
      "members": [
        "contextual-qa",
        "df-self-reflection",
        "free-response"
      ],
      "attributes": {
        "input": "Query → generate multiple diverse perspectives → compare → reflect",
        "output": "Cross-perspective refined answer",
        "cost": "moderate",
        "llm_calls": "N perspectives + 1 reflection",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "self-discover",
      "name": "Self-Discover",
      "description": "",
      "members": [
        "alg-auto-prompt-gen",
        "cap-deductive",
        "cap-planning",
        "cap-self-monitoring",
        "commonsense-reasoning",
        "df-decomposition",
        "logical-reasoning",
        "math-algebraic",
        "math-word-problems",
        "multi-hop-reasoning",
        "pc-intermediate-reasoning"
      ],
      "attributes": {
        "input": "Task → select relevant reasoning modules → adapt to task → compose into JSON reasoning structure → solve",
        "output": "Self-composed reasoning plan → structured solution",
        "cost": "high",
        "llm_calls": "3+ (select → adapt → implement)",
        "note": "Up to +42% over CoT, 10-40x less compute than CoT-SC",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "self-refine",
      "name": "Self-Refine Prompting",
      "description": "",
      "members": [
        "br-selfrefine-code",
        "br-selfrefine-various",
        "cap-self-monitoring",
        "code-gen",
        "df-iterative-refinement",
        "df-self-reflection",
        "dialogue",
        "free-response",
        "hallucination-reduction",
        "math-algebraic",
        "math-word-problems",
        "sentiment"
      ],
      "attributes": {
        "input": "Query → draft → self-critique → refined draft (iterative loop)",
        "output": "Iteratively refined output",
        "cost": "high",
        "llm_calls": "2+ per iteration",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "selfmem",
      "name": "Selfmem",
      "description": "",
      "members": [
        "alg-retrieval",
        "df-external-memory",
        "df-iterative-refinement",
        "free-response",
        "summarization"
      ],
      "attributes": {
        "input": "Iterative: memory pool of prior LLM outputs → selector picks best → next round",
        "output": "Progressively improved generation",
        "cost": "high",
        "llm_calls": "iterative",
        "source_papers": [
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "skeleton-of-thought",
      "name": "Skeleton-of-Thought (SoT)",
      "description": "",
      "members": [
        "cap-compositional",
        "cap-planning",
        "contextual-qa",
        "df-decomposition",
        "df-parallel-decomposition",
        "free-response",
        "pc-format-constraints",
        "summarization"
      ],
      "attributes": {
        "input": "Query → generate skeleton outline → parallel expansion of each point → assembly",
        "output": "Structured, parallel-generated response",
        "cost": "moderate",
        "llm_calls": "1 + N parallel expansions",
        "note": "ICLR 2024, 2x+ speedup, 60% quality maintained/improved",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "synthetic-prompting",
      "name": "Synthetic Prompting",
      "description": "",
      "members": [
        "br-synthetic-multi",
        "cap-inductive",
        "logical-reasoning",
        "math-algebraic",
        "math-word-problems",
        "pc-complexity-selection",
        "pc-intermediate-reasoning"
      ],
      "attributes": {
        "input": "Backward (LLM generates Q from chain) + forward (chain for Q) → select by complexity",
        "output": "Augmented exemplars → CoT answer",
        "cost": "high",
        "llm_calls": "many (synthesis + selection)",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "take-step-back",
      "name": "Take a Step Back Prompting",
      "description": "",
      "members": [
        "br-stepback-musique",
        "br-stepback-timeqa",
        "cap-abstraction",
        "cap-temporal",
        "cf-qa",
        "commonsense-reasoning",
        "df-context-augmentation",
        "math-algebraic",
        "math-word-problems",
        "multi-hop-reasoning",
        "physical-reasoning"
      ],
      "attributes": {
        "input": "Query → step-back question → abstract answer → grounded answer",
        "output": "High-level principle → detailed answer",
        "cost": "moderate",
        "llm_calls": "2-3 (may include retrieval)",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "talm",
      "name": "TALM (Tool Augmented Language Models)",
      "description": "",
      "members": [
        "alg-tool-use",
        "cap-planning",
        "cf-qa",
        "task-completion"
      ],
      "attributes": {
        "input": "Query → LLM emits tool-call tokens → API called → result appended",
        "output": "Tool-augmented text",
        "cost": "moderate",
        "llm_calls": "1 + API calls",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "thor",
      "name": "Three-Hop Reasoning (THOR)",
      "description": "",
      "members": [
        "cap-emotional-understanding",
        "df-aspect-opinion-sentiment",
        "sentiment"
      ],
      "attributes": {
        "input": "Query → identify aspect → determine opinion → infer sentiment",
        "output": "Aspect → Opinion → Sentiment label",
        "cost": "moderate",
        "llm_calls": 3,
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "thot",
      "name": "Thread of Thought (ThoT) Prompting",
      "description": "",
      "members": [
        "cf-qa",
        "df-self-reflection",
        "dialogue",
        "pc-intermediate-reasoning"
      ],
      "attributes": {
        "input": "Long chaotic context + query → segment analysis → answer",
        "output": "Section summaries → final answer",
        "cost": "moderate",
        "llm_calls": 2,
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "toolalpaca",
      "name": "ToolAlpaca",
      "description": "",
      "members": [
        "alg-tool-use",
        "task-completion"
      ],
      "attributes": {
        "input": "Query → simulated tool-use environment → generalized tool interaction",
        "output": "Tool-assisted answer",
        "cost": "moderate",
        "llm_calls": "1 + tools",
        "source_papers": [
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "toolformer",
      "name": "Toolformer",
      "description": "",
      "members": [
        "alg-tool-use",
        "cap-planning",
        "cf-qa",
        "task-completion"
      ],
      "attributes": {
        "input": "Query → self-supervised tool-call embedding in prompt",
        "output": "Tool-augmented response",
        "cost": "moderate",
        "llm_calls": "1 + tool calls",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "tot",
      "name": "Tree-of-Thoughts (ToT) Prompting",
      "description": "",
      "members": [
        "alg-backtracking",
        "alg-thought-evaluation",
        "alg-tree-search",
        "br-tot-game24",
        "br-tot-game24-vatsal",
        "cap-creative-generation",
        "cap-deductive",
        "cap-planning",
        "free-response",
        "logical-reasoning",
        "math-search-based"
      ],
      "attributes": {
        "input": "Query → tree of partial solutions explored via BFS/DFS",
        "output": "Best evaluated path → answer",
        "cost": "very_high",
        "llm_calls": "O(b^d) branching",
        "requires_large_model": true,
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "verify-and-edit",
      "name": "Verify-and-Edit (VE)",
      "description": "",
      "members": [
        "alg-retrieval",
        "cap-self-monitoring",
        "cf-qa",
        "df-verification",
        "multi-hop-reasoning",
        "table-qa",
        "truthfulness"
      ],
      "attributes": {
        "input": "CoT chains → SC finds uncertain → edit chains with external knowledge → re-generate",
        "output": "Fact-corrected rationale → answer",
        "cost": "high",
        "llm_calls": "SC + edit + generation + retrieval",
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": []
      }
    },
    {
      "id": "zero-shot",
      "name": "Zero-Shot Prompting",
      "description": "",
      "members": [
        "cap-compositional",
        "cap-inductive",
        "cf-qa",
        "code-gen",
        "free-response",
        "machine-translation",
        "ner",
        "nli",
        "pc-task-instruction",
        "sentiment",
        "text-classification"
      ],
      "attributes": {
        "input": "Query (instruction only)",
        "output": "Direct answer",
        "cost": "minimal",
        "llm_calls": 1,
        "requires_large_model": false,
        "source_papers": [
          "A Comprehensive Survey of Prompt Engineering Techniques - Debnath et al.txt",
          "A comprehensive taxonomy of prompt engineering techniques - Yao-Yang Liu et al.txt",
          "Survey of Prompt Engineering Methods - Vatsal et al.txt",
          "SystematicSurveyofPromptEngineering_Sahoo_et_al.txt"
        ],
        "fails_at": [
          "multi-hop-reasoning"
        ]
      }
    }
  ]
}