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Your vector search returns high similarity scores but irrelevant results. How do you debug it?

A 0.87 cosine score on a chunk that answers nothing is not a bug in your vector database. It is the embedding model telling you exactly what it was trained to tell you. Here is how to read that signal instead of thresholding it away.

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

A 0.87 cosine score on a chunk that answers nothing is not a bug in your vector database. It is the embedding model telling you exactly what it was trained to tell you. Here is how to read that signal instead of thresholding it away.

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LEARN THE BACKGROUND

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

Applied AI Engineering·RetrievalFree14mWhy your search misses the obvious answerPure vector search fails in two specific, predictable ways, and both have standard fixes that most first systems are missing. This lesson covers the two-stage design that turns a demo into something that finds the right passage.
UP NEXT ON YOUR JOURNEY
Next in this trackTool-calling vs code-acting agents: which action space to pick.Next in this trackYour single-turn evals pass but the agent falls apart by turn six. How do you catch that before launch?Next in this trackA multi-agent system beats your simple RAG pipeline by 15% on the benchmark. Do you ship it?
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