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
- Candidates for the AWS Certified Machine Learning Specialty certification
- 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:
Environment before
Environment after
Jun is a Cloud Labs Developer with previous experience as a Software Engineer and Cloud Developer. He holds the AWS Certified Solutions Architect and DevOps Engineer Professional certifications. He also holds the AWS Certified Solutions Architect, Developer, and SysOps Administrator Associate certifications.
Jun is focused on giving back to the growing cloud community by sharing his knowledge and experience with students and creating engaging content.