When an AI Agent gives a bad answer, the model is not always the first place we should look. When a...

Milvus on X: "When an AI Agent gives a bad answer, the model is not always the first place we should look. When an answer is weak, teams often look at the prompt, model, or agent logic first. But many issues start earlier: the system retrieved the wrong context, missed the right one, or https://t.co/ynozWWa7YD" / X

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When an AI Agent gives a bad answer, the model is not always the first place we should look. When an answer is weak, teams often look at the prompt, model, or agent logic first. But many issues start earlier: the system retrieved the wrong context, missed the right one, or filtered it out before the LLM saw it. That is why Simon Hearne's talk on visualizing vector search is worth watching. It makes the retrieval layer feel less like a black box. In SQL, EXPLAIN shows the plan, index, and why a query behaves a certain way. Vector search is messier. Results are shaped by embeddings, index choice, quantization, metadata filters, and the latency/recall tradeoffs teams make. Simon walks through examples that come up quickly in production: • Brute-force search is easy to trust, until the dataset gets large. • IVF, HNSW, and DiskANN each make different tradeoffs. • Quantization can help with speed and storage, but recall still needs checking. • Filters can remove the paths to the best matches. For teams building RAG or agents, the lesson is simple: debug retrieval directly. Track recall@k. Look at score distributions. Watch filter hit rates. Treat index and embedding changes as quality changes. This is the engineering work Milvus is built around: a production retrieval layer teams can tune, scale, and reason about. Watch Simon's visual explanation here:

youtube.com/watch?v=kY-il0…

3:30 PM · Aug 7, 2026

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