7 articles tagged with Search.
When RAG answers go sideways, the model usually isn't the problem. Here's the top-to-bottom checklist we run to find where retrieval actually breaks.
The top model on the MTEB leaderboard made our search worse and our bill bigger. Here's how we actually picked an embedding model for a real RAG system.
Our RAG answers kept citing the wrong paragraph even when the right one was retrieved. A cross-encoder reranker fixed relevance but added 180ms. Here's when that trade pays off.
Pure vector search kept missing exact matches like error codes and CLI flags. Adding BM25 back and fusing the two lifted our retrieval recall by 11 points.
Our support bot kept citing half a sentence and missing the answer that sat two lines below. The culprit wasn't the model, it was how we split the docs.
You can't improve retrieval you don't measure. The offline eval harness that lets us change embeddings, chunking, and rerankers with confidence instead of vibes — with the metrics that actually predict production quality.
Pure vector search misses exact-keyword queries. Pure BM25 misses semantic ones. Combining them with reciprocal rank fusion is the simplest large win in RAG retrieval.
