Understanding Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) is an AI architecture pattern that combines the reasoning capabilities of Large Language Models (LLMs) with external knowledge sources. Instead of relying solely on information learned during model training, RAG retrieves relevant information from documents, databases, APIs, or knowledge repositories and provides that information as context to the model before generating a response. Traditional LLMs only know what they learned during training unless new context is provided at runtime. They also cannot automatically access private PDFs, internal documents, databases, or company knowledge. RAG solves this by adding a retrieval layer that finds relevant information first, then gives that information to the language model before it answers. In simple terms, RAG means: search first, answer second. Why RAG Exists Reduce hallucinations by grounding responses in trusted data. Allow mod...

Building a Managed RAG Platform with Amazon Bedrock Amazon Bedrock provides managed services that simplify the implementation of Retrieval-Augmented Generation systems. Instead of building chunking, embeddings, retrieval, and orchestration from scratch, organizations can use Knowledge Bases for Amazon Bedrock with managed foundation models. Key AWS Services Amazon S3 Amazon Bedrock Knowledge Bases for Amazon Bedrock Amazon OpenSearch Serverless, commonly used as the managed vector store Optional vector stores such as Aurora PostgreSQL Serverless, Amazon S3 Vectors, Neptune Analytics, or a supported existing vector store Amazon Textract AWS Lambda Amazon ECS Amazon API Gateway Amazon CloudWatch Reference Architecture PDF Upload | v Amazon S3 | v Knowledge Base for Amazon Bedrock | +--> Chunking | +--> Embeddings | +--> Vector Storage | v OpenSearch Serverless User Question | ...

Building a Self-Managed RAG Platform A self-managed RAG platform gives an organization direct control over document processing, embeddings, retrieval, model serving, infrastructure, security, and optimization. Teams usually choose this approach when they need specialized models, strict data-control requirements, custom retrieval logic, or potential cost savings at high scale. The tradeoff is operational responsibility. Unlike a managed platform, the team must own model hosting, scaling, monitoring, evaluation, security, upgrades, and reliability. Core Architecture Components Document Processing Service Chunking Service Embedding Service Vector Database Retriever Service Reranker Service LLM Inference Service Chat Application Hybrid Retrieval Layer, optional for vector search, BM25 keyword search, metadata filters, and reranking Reference Architecture Ingestion Flow PDF / Documents | v Document Processing / OCR | v Chunk...