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A practical checklist to improve RAG retrieval with hybrid search (BM25 + vectors), rerankers, and query rewriting. Reduce “wrong chunk wins” by tuning filters, top-k, and evaluation.
A practical checklist to make RAG answers trustworthy. Enforce citations, track doc_ids, handle conflicts, and prevent the model from inventing sources. Includes logging and UX patterns that scale.
A practical checklist to make AI apps reliable under rate limits and transient failures. Learn how to set timeouts, retries, backoff, idempotency, and fallbacks without creating hidden loops or runaway costs.