8 articles tagged with Embeddings.
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.
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.
Embedding indexes degrade silently. The signals that catch drift, how often to re-embed, and the operational patterns we built after one quiet quality regression.
Embeddings turn text into numbers a computer can compare. Here's the working mental model, a runnable Python example, and where embeddings fit in real apps.
