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Building Generative AI Applications Using Amazon Bedrock - AWS
This course is designed for application developers interested in building generative artificial intelligence (generative AI) applications using either the Amazon Bedrock APIs or AWS-LangChain integration. In this course, you will explore the architecture patterns and implementations to support generative AI use cases such as generating and summarizing text, retrieval augmented generation (RAG), and question answering.
Objectives
- Identify the components of a generative AI application and the options to customize a foundation model (FM)
- Describe Amazon Bedrock foundation models, inference parameters, and key Amazon Bedrock APIs
- Describe the architecture patterns that can be used to build generative AI applications
- Identify Amazon Web Services (AWS) offerings that help with monitoring, securing, and governing your Amazon Bedrock applications
- Describe LangChain components such as prompt templates, chains, retrievers, and agents
- Apply LangChain components to Amazon Bedrock models to build and test use cases such as text and code generation, summarization, RAG, and question answering
- Use Amazon Bedrock Knowledge Bases to implement RAG applications using best practices
- Use Amazon Bedrock Agents with Amazon Bedrock Knowledge Bases and Amazon Bedrock Guardrails for agent applications
Resource details
| Contributed by: | Generative AI |
| Authored by: |
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| Licence: | More information on licences |
| First contributed: | 02 July 2025 |
| Audience access level: | Full user |
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