Bedrock Knowledge Base Advanced RAG with Terraform: Chunking, Hybrid Search, and Reranking 🧠

Fixed-size chunking is just the starting point. Semantic chunking, hierarchical retrieval, hybrid search, reranking, and metadata filtering turn a basic RAG pipeline into a production system. All configurable in In RAG Post 1 , we deployed a basic Bedrock Knowledge Base with fixed-size chunking. It works, but retrieval quality is mediocre. Your users ask complex questions and get incomplete answers. The model pulls in irrelevant chunks while missing the ones that matter. The fix isn't a better model. It's better retrieval. Bedrock Knowledge Bases supports four chunking strategies, hybrid search, reranking models, metadata filtering, and query decomposition. All of these are configurable through Terraform and the retrieval API. This post covers the production patterns that separate a demo from a system your users actually trust. 🎯 🧱 Chunking Strategies: Choosing the Right One Chunking is the single biggest lever for RAG quality. How you split documents determines what gets retrieved. Be