Lesson deck
Better retrieval flashcards
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- Front
- Why hybrid search?
- Back
- BM25 catches exact terms and IDs; vectors catch paraphrase. Fuse the lists (for example with Reciprocal Rank Fusion), then rerank.
- Front
- What does Reciprocal Rank Fusion compute, and why use it?
- Back
- score(d) = sum over lists of 1/(k + rank_i(d)), k = 60 by default (Cormack et al., SIGIR 2009). Ranks only, so no calibration of incompatible score scales.
- Front
- Bi-encoder vs cross-encoder?
- Back
- Bi-encoder embeds query and document separately (fast, precomputable). Cross-encoder scores the pair jointly (more accurate, one call per pair), so use it to rerank a shortlist.
- Front
- Why doesn't reranking fix a recall problem?
- Back
- It only reorders candidates it is given. If the right chunk is not in the shortlist, widen or change the first stage (more candidates, BM25, better query).
- Front
- What can go wrong with strict metadata pre-filters in an approximate index?
- Back
- Some ANN indexes lose recall or speed when a filter is very selective. Test your database with realistic filters. The filter value should come from the authenticated identity, not from user input.
- Front
- Name two costs of query transformations.
- Back
- Each adds an LLM call (latency, money) and can drift from the user's intent. Gate them to the queries that need them.
- Front
- What does MMR do?
- Back
- Maximal marginal relevance picks chunks that are relevant and different from those already chosen, so top-k is not near-duplicates.