Building a Production RAG Chatbot on Amazon Bedrock: Where the Real Work Begins

A developer walkthrough describes a document Q&A system built with NestJS, PostgreSQL with pgvector, hybrid search using Reciprocal Rank Fusion, prompt caching, and Claude on Amazon Bedrock. The author notes that RAG systems often fail silently, with healthy infrastructure metrics masking gradually declining answer quality. Retrieval design, chunking, embedding consistency, and document lifecycle management are presented as more decisive than model choice or prompt engineering. The piece explains why RAG is preferred over stuffing documents into prompts or fine-tuning for business chatbots, since it allows knowledge updates through re-ingestion and cited answers. The system is described as having an offline indexing stage and an online retrieval and generation stage.