You’re right—thanks for catching this. The second “paragraph” was meant to be a comparison table, but its formatting broke. Here’s what it should say:
- Main output: Jev AI returns choices, scores, yes/no judgments, probabilities, and confidence signals. Generative LLMs produce text, code, or open-ended structured content. Rule-based code returns deterministic values from explicit conditions.
- Best fit: Jev AI suits classification, routing, priority and risk checks, and tool gating. Generative LLMs suit writing, summarization, Q&A, and complex reasoning. Rules suit stable conditions that need no semantic interpretation.
- Interface: Jev AI takes state and typed questions; an LLM takes a prompt and context; rule-based code uses explicit conditions.
- Uncertainty: Jev AI exposes probability and confidence signals. LLM outputs usually need custom validation. Rules do not involve model probabilities.
- Role in an application: Jev AI acts as a decision layer, an LLM handles generation or reasoning, and rules provide deterministic execution.
Sorry for the confusing formatting, and thanks again for pointing it out.