Show Notes
This episode addresses a retrieval failure that has nothing to do with your index and everything to do with the query itself. We explore the vocabulary gap between how people ask questions and how documents are written, and why even strong embedding models cannot always bridge it. We break down three techniques that fix the query before the search runs: query rewriting to reformulate casual language into formal search terms, HyDE which generates a hypothetical answer and uses that as the search query instead of the question, and multi-query expansion which generates multiple phrasings to cast a wider retrieval net. We also cover step-back prompting for queries that need broader conceptual grounding before searching. By the end you will understand why the question itself is often the highest-leverage thing to improve in a retrieval pipeline.
More from The AI Concepts Podcast
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