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hardCohereGleanHarvey

Off-the-shelf embeddings retrieve poorly on your domain. How do you improve retrieval accuracy?

A model that tops MTEB can still flop on your jargon-heavy corpus, and most teams jump straight to the expensive fix. There is a ladder, and the cheap rungs are the ones people skip. Here is the order to climb it.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

A model that tops MTEB can still flop on your jargon-heavy corpus, and most teams jump straight to the expensive fix. There is a ladder, and the cheap rungs are the ones people skip. Here is the order to climb it.

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LEARN THE BACKGROUND

These lessons teach the material this question tests, in order and from the beginning.

Applied AI Engineering·RetrievalFree12mWhen retrieval is the wrong answerRetrieval has become the reflex, and it is wrong often enough to be worth a lesson. Four situations where something simpler or entirely different is the better system, and the decision procedure that gets you there in ten minutes.
UP NEXT ON YOUR JOURNEY
Next in this trackYour RAG keeps retrieving near-duplicate chunks, wasting the context window. How do you diversify results?Next in this trackYour agent loops forever or never finishes the task. How do you bound and control agent execution?Next in this trackA tool your agent depends on returns errors or garbage. How do you make the agent robust to tool failures?
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