Enhancing Generative AI Models With Retrieval-Augmented Generation (RAG)

Large language models (LLMs) are already proven to be capable of generating human-like responses, but these responses can be enhanced to provide more accurate and relevant information. Retrieval-Augmented Generation (RAG) is a technique that combines the strengths of LLMs and information retrieval systems to generate text that is both fluent and factually accurate.

In this lab, you will learn about Retrieval-Augmented Generation, its use cases, and common components. You will also learn how to implement RAG in a Python application.

Learning objectives

Upon completion of this beginner-level lab, you will be able to:

• Explain the concept of Retrieval-Augmented Generation (RAG) and its use cases
• Implement RAG in a Python application using LangChain and Amazon Bedrock

Intended audience

• Cloud Architects
• Software Engineers

Prerequisites

Familiarity with the following will be beneficial but is not required:

• Python
• Amazon Bedrock

The following content can be used to fulfill the prerequisites:

• Employing Generative AI for Development With Amazon Bedrock
• Optimizing Prompts For Large Language Models Using Amazon Bedrock

Updates

July 16th, 2025 - Updated Jupyter Notebook to use the latest Amazon Nova Lite model

December 16th, 2024 - Resolved an issue preventing the lab from deploying