Moving Beyond Simple Vector Search: Why Hybrid Search is Essential for RAG

As LLMs continue to dominate the landscape, Retrieval-Augmented Generation (RAG) has become the go-to architecture for grounding AI in private data. However, many developers hit a wall when their RAG systems fail to retrieve context-specific details. The solution? Hybrid Search .

The Limitation of Dense Vectors

Dense vector embeddings are excellent at capturing semantic meaning. They allow an AI to understand that 'canine' and 'dog' are related. However, they struggle with:

Keyword matching: Precise product SKUs or acronyms.

Rare terminology: Domain-specific jargon that doesn't appear in broad training sets.

Enter Hybrid Search

Hybrid search combines Semantic Search (Vector) with Lexical Search (BM25/TF-IDF).…