Hybrid Search
Also known as: Hybrid retrieval, Sparse-dense retrieval
Combine BM25 (keywords) + dense embeddings (meaning); better recall than either alone.
A retrieval strategy combining lexical (BM25) and semantic (embedding) scores via reciprocal rank fusion or weighted sum. Captures both exact-keyword hits and conceptual matches that either method alone would miss.
In practice
The default production RAG setup post-2024. Interviews probe RRF, alpha tuning, and when pure dense suffices.
How it compares
Hybrid search is BM25 + dense embeddings combined; BM25 alone is the lexical half.
Related topics
Related terms
Retrieval-Augmented Generation (RAG)
Fetch relevant docs at inference time and stuff them into the prompt so the model can answer from real data.
Embeddings
Dense numeric vectors that capture meaning; close vectors = similar text.
Vector Database
A database tuned for fast approximate nearest-neighbor search over millions of high-dimensional vectors.
Chunking
Cutting documents into retrievable pieces before embedding them.
Context Window
The max number of tokens a model can attend to at once.
Hallucination
When a model confidently makes up something that isn't true.