Stop telling your RAG bot not to hallucinate. Make it impossible.

Short summary

Instead of asking RAG models not to hallucinate, architect it out by adding a confidence gate in retrieval: if candidates don't clear a threshold (e.g., cosine sim > 0.45), return empty results so the model has nothing to fabricate. The author validates this with golden set testing, achieving 100% refusal accuracy while maintaining citation and version-correctness guarantees. The lesson: move guarantees from prompts (hopes) to architecture (constraints).

• •Refusal gates belong in retrieval, not prompts—deny the model source material to make hallucination impossible
• •Use confidence thresholds (e.g., cosine similarity > 0.45) to filter candidates; empty results force honest refusal
• •Validate with golden sets: the author achieved 100% accuracy on refusal, citation, and version-correctness

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