What is Hybrid Retrieval?

Retrieval that combines signals from different methods, commonly lexical matching and dense-vector similarity, before merging or reranking results.

Why does Hybrid Retrieval matter?

Exact identifiers, rare terms, and semantic paraphrases behave differently, so one retrieval signal can miss useful evidence.

Retrieve candidates with both BM25-style keyword search and embeddings, merge their ranks, then rerank the combined set for the user query.

Hybrid retrieval combines candidate signals. A reranker applies a second relevance model to candidates already retrieved.

Start with

Advanced RAG (Chunking, Reranking, Hybrid Search)
Basic RAG retrieves the top-k most similar chunks. That works for simple questions. It falls apart for multi-hop reasoning, ambiguous queries, and large corpora.
Phase 11: LLM Engineering

Lessons that name Hybrid Retrieval in a title or section

Hybrid Retrieval with BM25 and Dense Embeddings
Lexical and semantic retrieval fail on opposite query distributions. Hybrid retrieval with reciprocal rank fusion does not interpolate, it votes - and the vote wins on every query class.
Phase 19: Capstone Projects

Semantic SearchRetrieval that represents a query and candidates in an embedding space and ranks candidates using a vector-similarity function.
RerankerA second-stage model or scoring function that reorders a small candidate set using a richer comparison between the query and each candidate.
RAG (Retrieval-Augmented Generation)A system pattern that retrieves evidence relevant to a request and supplies selected content to a generative model before it answers or…
EmbeddingA learned mapping from discrete items (words, images, users) to dense vectors in continuous space, where similar items end up close together
BM25A lexical ranking function that scores a document from query-term matches while accounting for term rarity, repeated occurrences, and…
Dense RetrievalFirst-stage retrieval that embeds queries and candidates into vector representations and ranks candidates by a similarity function.
Reciprocal Rank Fusion (RRF)A rank-fusion method that combines several result lists by summing contributions that decrease with each item's rank in each list.
Vector DatabaseA storage and indexing system that supports nearest-neighbor queries over vector representations, often with metadata filtering,…