Intro to RAG with Amazon AgentCore

Why we picked it

In this hands-on workshop, you'll go from raw embeddings to a fully deployed, production-ready RAG agent on AWS — no prior RAG or AgentCore experience needed. We'll build a real document Q&A system, stage by stage: * **Basic RAG from scratch** — embed documents yourself with Amazon Titan Text Embeddings V2, store them in a local FAISS index, retrieve with cosine similarity, and generate answers with Claude. * **Bedrock Knowledge Bases** — swap the DIY pipeline for a fully managed service that handles chunking, embedding, and indexing (OpenSearch Serverless) from an S3 data source, with hybrid search and metadata filtering. Compare the results against your hand-built version. * **AgentCore Agent** — wrap your RAG into a Strands agent, containerize it, push to ECR, and deploy to AgentCore Runtime with session isolation, auto-scaling, and streaming. * **Production hardening** — add AgentCore Memory for conversation continuity, instrument with OpenTelemetry for traces in CloudWatch, run a