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README.md

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+---
+language:
+- en
+license:
+- mit
+multilinguality:
+- monolingual
+size_categories:
+- n<1K
+tags:
+- rag
+- retrieval-augmented-generation
+- evaluation
+- benchmark
+- question-answering
+- faithfulness
+pretty_name: RAG Evaluation Benchmark & QA Test Suite
+---
+
+# RAG Evaluation Benchmark & QA Test Suite 📚
+
+A curated ground-truth evaluation benchmark designed to assess Retrieval-Augmented Generation (RAG) pipelines across three primary dimensions:
+1. **Context Recall**: Did the retriever fetch the passage containing the necessary ground-truth facts?
+2. **Faithfulness**: Is the generated answer supported strictly by the retrieved evidence without hallucinated extrapolations?
+3. **Answer Relevancy**: Does the generated synthesis directly answer the question?
+
+- **Associated Space**: [RAG Engine Architecture Showcase](https://huggingface.co/spaces/Jayan12321/rag-engine)
+- **GitHub Repository**: [JayanGupta/RAG-Engine](https://github.com/JayanGupta/RAG-Engine)
+- **Author**: [Jayan Gupta](https://huggingface.co/Jayan12321)
+
+---
+
+## 📊 Dataset Schema
+
+| Field | Type | Description |
+|---|---|---|
+| `question_id` | string | Unique identifier for the evaluation item. |
+| `question` | string | User query spanning technical domains (quantum, space, clean energy). |
+| `ground_truth_answer` | string | Verified, objective factual answer derived from the source knowledge corpus. |
+| `evidence_doc` | string | Title of the source document containing the authoritative facts. |
+| `question_type` | string | Type of cognitive task: `factoid`, `synthesis`, or `comparative`. |
+
+---
+
+## 💻 Quickstart with Python
+
+```python
+from datasets import load_dataset
+
+# Load directly from Hugging Face
+dataset = load_dataset("Jayan12321/rag-evaluation-benchmark")
+
+# Inspect sample evaluation question
+sample = dataset["train"][0]
+print("Question:", sample["question"])
+print("Ground Truth:", sample["ground_truth_answer"])
+print("Evidence Document:", sample["evidence_doc"])
+```
+
+---
+
+## 🛠️ Usage in RAG Benchmarking
+This dataset is designed to be paired with heuristic faithfulness checkers or RAGAS-style evaluation frameworks to objectively score retriever and generator configurations.