Module 6: RAG | Reranking - The Second Stage That Gets Retrieval Right
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Module 6: RAG | Reranking - The Second Stage That Gets Retrieval Right
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This episode addresses the gap between finding candidate chunks and finding the right ones. We explore the bi-encoder bottleneck, why compressing text into a single vector for comparison loses critical nuance, and how cross-encoders fix this by reading the query and document together in a single forward pass. We introduce ColBERT as a powerful middle ground between speed and accuracy through token-level late interaction, walk through the production tooling landscape including Cohere Rerank, BGE models, and RAGatouille, and close by stitching hybrid search and reranking into a complete three-stage retrieval funnel. By the end you will understand why two-stage retrieval is now the standard architecture for any serious RAG pipeline.
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