aws-samples/amazon-bedrock-samples resource

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1496 stars · 723 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of Jupyter notebook examples and guides for getting started with Amazon Bedrock, AWS's managed generative AI service. It covers foundation model invocation, prompt engineering, agents, RAG, embeddings, multimodal use cases, evaluation, and productionization patterns.

Use cases

• learn how to use amazon bedrock
• build a rag application with bedrock knowledge bases
• examples of calling claude or titan models on aws
• get started with generative ai agents on bedrock
• learn prompt engineering techniques for bedrock models
• use embeddings models on amazon bedrock
• evaluate and monitor bedrock model outputs
• move a bedrock proof of concept to production

When to choose

• you are building on Amazon Bedrock specifically and want official, maintained sample code
• you want guided notebooks covering the full Bedrock feature set from basics to production
• you need reference implementations of RAG, agents, or embeddings with Bedrock models

When to avoid

• you use a different LLM provider or want provider-agnostic examples
• you need a production-ready library or framework rather than example notebooks
• you cannot access AWS or do not have Bedrock model access enabled

Facets

learning-resource · maturity active

rag prompt-engineering agent-framework llm-inference large-language-models artificial-intelligence cloud-computing tutorials python cloud amazon-bedrock aws jupyter-notebooks generative-ai sample-code foundation-models knowledge-bases langchain retrieval-augmented-generation ai-agents jupyter

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For agents

markdown · JSON · MCP: product_card(name="aws-samples/amazon-bedrock-samples")