Hallucination

Design a fine-tune that teaches a model to refuse when the retrieved context lacks the answer.

Why 'fine-tune on our company docs' usually fails: and what to do instead

Best architecture for a chatbot over a frequently-updated company knowledge base?

Why can fine-tuning on domain facts INCREASE hallucination?

Why does fine-tuning on company docs often raise hallucination instead of lowering it?

Define faithfulness in RAG evaluation and distinguish it from answer relevance

Clarify the relationship between unfaithfulness and hallucination in RAG systems

Is 'unfaithful' the same as 'hallucinated'? What is the relationship between these concepts?

Which metric best measures whether a RAG answer is grounded in the retrieved context?

What does RAG primarily help with in LLM-based applications?