Core

Hybrid Search and Reciprocal Rank Fusion

Vector search alone grasps meaning but drops exact terms (codes, names, SKUs); keyword search (BM25) alone locks onto exact terms yet ignores synonyms and intent. Hybrid search runs the two together and fuses their outputs, with Reciprocal Rank Fusion offering an easy way to merge rankings without reconciling incomparable scores. Applied-AI interviews cover it because production retrieval is nearly always hybrid, so understanding why (and how to fuse) shows genuine RAG experience.

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RELATED CONCEPTS

Vector Search and ANN Indexes Reranking The RAG Pipeline Embeddings

PRACTICE THIS IN REAL QUESTIONS

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?→

COMPANIES THAT ASSUME THIS

OpenAI Anthropic Google Microsoft Scale AI Cognition

NEXT IN RETRIEVAL & AGENTSGraphRAG and Knowledge-Graph Retrieval→