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Core
Hybrid Search and Reciprocal Rank Fusion
Pure vector search captures meaning but misses exact terms (codes, names, SKUs); pure keyword search (BM25) nails exact terms but misses synonyms and intent. Hybrid search runs both and fuses the results, and Reciprocal Rank Fusion is the simple way to merge their rankings without calibrating incomparable scores. Applied-AI interviews probe it because production retrieval is almost always hybrid, and knowing why (and how to fuse) signals real RAG experience.
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RAG & Agent System DesignHow do you combine lexical (BM25) and semantic (vector) retrieval, and what is Reciprocal Rank Fusion?→RAG & Agent System DesignDesign a production RAG system over 10M documents serving ~1,000 QPS at sub-second latency.→RAG & Agent System DesignBuild a small in-memory document indexer and retriever from scratch (inverted index + BM25), then add a vector option.→RAG & Agent System DesignHow do you choose chunk size and decide between dense, sparse (BM25), and hybrid retrieval?→System Design for AI in ProductionDesign a multimodal (text and image) search system for a large e-commerce catalog.→RAG & Agent System DesignContext windows are now huge. When do you just stuff everything in context instead of building RAG?→
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