hallucination-evaluatorlisted

"It sounds confident" is not "it's correct." A RAG or grounded-generation feature can produce fluent, authoritative prose that the retrieved source never supports — and fluency is uncorrelated with faithfulness, so you cannot eyeball it. This skill makes hallucination measurable: it defines the standard precisely, decomposes each answer into atomic claims, checks each claim for entailment against the source, builds a labeled eval set that includes the should-abstain cases, splits retrieval failures from generation failures, and produces a groundedness score you can gate releases on. ## When to use this skill - A RAG/LLM feature is making confident claims that turn out to be wrong, and you can't tell how often. - Before shipping anything that must be factual — support answers, summaries of provided docs, extraction over a source. - You want a groundedness gate in evals/CI so a regression in faithfulness blocks the release instead of surfacing in production. - A summary, citation, or "based on the document…" answer is adding facts the document doesn't contain. - You need to know *why* it's wrong — bad retrieval vs. the model ignoring good retrieval — because the fix differs. ## Instructions 1. **Define the standard precisely — faithfulness, not world-truth.** In RAG/grounded generation, a hallucination is a claim **not entailed by the retrieved context (the source you gave the model)**. This is *faithfulness*, and it is distinct from *factual accuracy against the world*. A c