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RAG evaluation and hallucination flashcards
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- Front
- Does RAG eliminate hallucination?
- Back
- No. It reduces it by grounding answers, but the model can still ignore, misread, or blend retrieved context with unsupported claims.
- Front
- Name two hallucination-mitigation techniques for RAG.
- Back
- An explicit "answer only from context" instruction with citations, and a retrieval-quality gate that refuses to answer when the best match score is too low.
- Front
- Three ways a model fails even with the right passage in the prompt?
- Back
- It ignores the context, misreads it, or blends it with plausible claims the passage never stated.
- Front
- Why is a copied similarity-score threshold risky?
- Back
- Scores are not comparable across embedding models or query types. Calibrate the gate on a labelled set and track both wrong refusals and wrong answers.
- Front
- Citations in the answer: what do they not guarantee?
- Back
- Presence of a citation does not mean the cited chunk supports the claim. Check citation accuracy.
- Front
- How do you classify a failure from a trace?
- Back
- Gold chunk not retrieved: retrieval. Gold chunk in prompt but answer contradicts it: generation. Answer follows a wrong or stale chunk: source.
- Front
- On a 30-question golden set, what is one extra correct answer worth, and what does it mean?
- Back
- About 3.3 percentage points. A one-question change is weak evidence; enlarge the set and inspect what changed.