Generate synthetic data for evaluating RAG systems using Amazon Bedrock

This article discusses how to generate synthetic data for evaluating Retrieval Augmented Generation (RAG) systems using Amazon Bedrock.

Specifically, the article covers:

• Fundamentals of RAG evaluation and the need for synthetic data
• An overview of the solution for generating synthetic data
• Loading and preparing data from a source like PDF documents
• Using LLMs like Anthropic's Claude to generate questions, answers, and refine them
• Automating the dataset generation process
• Improving the dataset using critique agents
• Best practices for generating synthetic datasets
• Conclusion and limitations of synthetic data generation