Embeddings

You'll learn what an embedding is, why embedding search works well for document RAG, why it often picks the wrong file in a codebase, and how to check its results with grep, call graphs and git history.

You'll learn what a vector database stores, how it finds results through chunking, embedding, indexing and querying, where it fits in an agent stack, and why live code needs grep, call graphs and freshness checks.

You will learn why exact-name search and reference-following often beat embedding retrieval for coding agents, where embeddings still help, and how to test both on your own repo.

You'll learn how a mid-sentence chunk split causes a RAG pipeline to confidently deny a fact that's actually in the source docs, and how to find and fix that failure in your own pipeline.

Learn what actually changes in your retrieval stack when you move from keyword search to embeddings and vector indexes, and why hybrid search is usually the right answer.

The relevance bug everyone blames on embeddings is usually one missing pipeline stage away from fixed — here's the two-line diagnostic that tells you which problem you actually have.

Three weeks of swapping embedding models and tuning rerankers won't fix a retriever that's been fed half a function signature — here's the 10-minute check that tells you if chunking, not your model, is the real problem.

It's not your prompt, your retriever, or your reranker — it's the embeddings you set once and never looked at again, and here's the diagnostic that catches it before it becomes an incident.

A vector search handed an agent a 0.89 confidence score and the wrong function — here's why embeddings can't tell 'looks like auth' from 'is auth,' and what to check before an agent acts on either.

Nine times out of ten, the chunk your model needs is already sitting in your retrieved set — just ranked too low to ever get read, and there's a one-hop fix that costs almost nothing to bolt on.

A cosine similarity score feels like proof of quality — until an exact SKU search comes back empty, and you realize 'semantic' was never something you'd actually measured.

The answer your user needed didn't get lost in search — it got cut in half before your embedding model ever laid eyes on it, and there's a 20-minute check that proves it.

Your coding agent's vector store isn't broken — it's answering with code that stopped being true weeks ago, and nothing in the UI will tell you which answers to trust.

If your retrieval eval scores refuse to budge no matter which embedding model you throw at them, the fix isn't a bigger model — it's the chunk boundary that quietly amputated your answer before encoding ever happened.