RAG combines retrieval and generation so model answers are grounded in your approved documentation. Amazon Bedrock Knowledge Bases offers two paths:
Managed Knowledge Base (recommended) โ AWS handles ingestion, chunking, embedding, re-ranking, and storage auto-scaling. Built-in connectors cover Amazon S3, SharePoint, Confluence, Google Drive, OneDrive, and Web Crawler, with document-level ACL filtering at query time and Smart Parsing for PDFs, PPTX, DOCX, and embedded visuals.
Customer-managed Knowledge Base โ you own the vector store (OpenSearch Serverless, Aurora, Neptune) and the full ingestion/indexing pipeline for maximum control.
Ingest source documents (or connect a structured/graph data source).
Create embeddings for semantic retrieval (multimodal embeddings for image-aware search).
Fetch top relevant chunks at query time, optionally via agentic multi-hop retrieval.
Compose a grounded prompt with citations.
Managed Knowledge Bases integrate natively with AgentCore Gateway, so any MCP-compatible agent framework can discover and call your knowledge base as a tool with no custom glue code. RAG reduces hallucination risk and improves trust when citations are surfaced.
โจ HANDS-ON LABDesign a Retrieval Pipeline
โญ +150 XP
Map documents, embeddings, retrieval, and response synthesis for a support assistant.
1Define your corpus folders for ingestion.
2Write a retrieval acceptance checklist.
lab-sandbox โ simulated environment
INFINITY LAB SANDBOX v2.6 โ simulated shell
Type the command for the current objective. Helpers: "hint", "solution", "clear".
$
OBJECTIVE 1 / 2 โ type "hint" if stuck
๐งช Knowledge Check
Press 1-4 to select1 of 3
What is the biggest reliability gain from RAG?
Infinite context size
Grounded responses from approved corpora
No need for prompts
No need for evaluation
RAG Architecture in Bedrock Tutorial | Knowledge Bases and RAG โ AWS Bedrock Academy