AWS-Based Enterprise RAG System Combines Hybrid Search and Bedrock for Scalable AI

Developer Ved Prajapati has outlined an enterprise-grade Retrieval-Augmented Generation (RAG) architecture built on AWS, designed to ground generative AI responses in external, organisation-specific knowledge. The system leverages Amazon Bedrock and Titan Embeddings for vector generation, and pairs semantic vector search with keyword-based retrieval through a hybrid search strategy. A FastAPI layer exposes application functionality, while workloads run on Amazon EKS with a React frontend. Rather than treating RAG as a simple vector similarity problem, the architecture breaks the pipeline into independently optimisable stages — including document ingestion, chunking, embedding, ranking, and context construction. The design aims to improve retrieval quality and scalability for production AI applications on cloud-native AWS infrastructure.