Module 6: RAG | Dense and Sparse Search - Why Vector Search Alone Is Not Enough
The AI Concepts PodcastShow Notes
This episode addresses one of the most common gaps in RAG pipelines, relying solely on semantic search. We explore how dense retrieval works and where it excels, then introduce sparse retrieval with BM25 and why it catches what vector search misses entirely, particularly exact identifiers like part numbers, codes, and proper nouns. We break down how hybrid search combines both approaches using Reciprocal Rank Fusion, why it consistently outperforms either method alone, and how modern vector databases like Weaviate, Pinecone, and Qdrant support this natively. By the end you will understand why the best retrieval systems are not choosing between semantic and keyword search but running both.
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