A developer encountered persistent hallucinations in their retrieval-augmented generation (RAG) application, despite RAG's intended purpose of reducing such errors. The issues stemmed from overly large text chunks, an over-reliance on top-k similarity for retrieval without reranking, and a lack of explicit instructions for the model to state when it lacked information. By implementing semantic chunking, adding a cross-encoder reranking step, and refining the prompt to allow for AI
RANK_REASON [lever_c_demoted from research: ic=1 ai=1.0]
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