Title: Fixing RAG Retrieval Errors with CRAG and Hybrid Search |...
URL Source: https://www.bestblogs.dev/status/2041592092133310648
Published Time: 2026-04-07 19:01:00
Markdown Content: High cosine similarity doesn't mean the document answers the question. Ask about a v2 API endpoint and the system retrieves v1 docs — semantically close, completely wrong in practice. Do this at scale and you get another problem: bad outputs get written back into memory, retrieved again on the next similar query, and the errors compound over time.
𝗖𝗥𝗔𝗚 fixes this with an evaluation step between retrieval and generation. The basic flow:• Retrieve top docs from Milvus
• Score each one: does it actually answer the query?
• Route by confidence — refine and use, supplement with a web search, or discard and fall back entirely
• Pass the cleaned-up context to the model
On the Milvus side, hybrid retrieval (dense + BM25) matters here because pure vector search misses exact-match content like version strings or product SKUs — exactly the kind of detail where retrieval errors hurt most.
We wrote a full tutorial covering the setup end-to-end, including the evaluator logic and multi-tenant isolation.
Learn momilvus.io/blog/fix-rag-r…zu3K