Retrieval Strategy Design: Vector, Keyword, and Hybrid Search

This article explains how to design a modern retrieval strategy for AI systems, especially Retrieval-Augmented Generation (RAG). The focus is not only on definitions, but on engineering trade-offs, system architecture, and practical defaults . The target audience is backend engineers who can already use embeddings, but want to design reliable and controllable search systems . 1. Where Retrieval Strategy Fits in the System A typical modern retrieval pipeline looks like this: User Query ↓ Query Rewrite / Intent Analysis ↓ Multi-Channel Retrieval (Vector / Keyword / Metadata) ↓ Hybrid Merge ↓ Top-K Limiting ↓ Score Threshold Filtering ↓ (Optional) Reranking ↓ LLM Generation Concepts like vector search , hybrid search , Top-K , and threshold filtering are not isolated features. They work together inside the recall and filtering stages of this pipeline. 2. Vector Search: The Semantic Recall Layer 2.1 What Vector Search Solves Vector search addresses the problem of semantic mismatch : The us