Core
Hybrid Search (Lexical + Vector)
Hybrid search runs a keyword retriever (BM25) and a dense vector retriever side by side, then merges their result lists, because each one misses cases the other catches. Vectors lose exact codes and rare jargon, BM25 loses paraphrase, and combining them with Reciprocal Rank Fusion usually beats either alone.
a free account unlocks the core curriculum tier · no card
Related course lessons
Related concepts
Practise this in questions
RAG & Agent System DesignKeyword search vs vector search: what does each actually buy you in a RAG system? →easy · FreeRAG & Agent System DesignDesign a hybrid retrieval stack: BM25, vectors, and a reranker. What does each stage rescue? →medium · FreeLLM & GenAI FundamentalsWhat are embeddings, and how do they make semantic search work? →easy · FreeLLM & GenAI FundamentalsWhy does vector search retrieve 'related but wrong' results, and how do you fix it? →medium · Free with sign-inCoding & DSAFind the k most similar items in a 10M-vector index without a hosted service. →hard · PremiumRAG & Agent System DesignThe customer's corpus grew from one million documents to ten million and retrieval p99 tripled. Your code did not change. Find out why. →hard · Premium
