OratorLab
📚 AI Agents Road Map 2026 / Topic 6: RAG (Retrieval-Augmented Generation) / FREE SUBTOPIC

Hybrid Search: Dense Vector + Sparse Keyword Retrieval

Understand how production-grade AI agents retrieve exact numbers, legal clauses, and semantic concepts using Reciprocal Rank Fusion (RRF).

⚡ Free Course Preview

Open in Interactive Course Player

Listen to voice explanations, test sample queries, and ask our live AI Expert.

▶ Launch Free Preview

Why Pure Vector Search Fails & Hybrid Search Wins

Dense vector search excels at high-level semantic meaning (e.g. mapping "laptop malfunctioning" to "hardware repair"), but often fails on exact model numbers, acronyms, and alphanumeric SKUs like ERR_504_TIMEOUT. Hybrid Search combines BM25 keyword matching with vector cosine similarity, then reconciles the ranking using Reciprocal Rank Fusion:

RRF_Score(d) = Σ [ 1 / (k + rank_dense(d)) ] + Σ [ 1 / (k + rank_sparse(d)) ]