aws-samples/genai-system-evaluation

A set of examples demonstrating how to evaluate Generative AI augmented systems using traditional information retrieval and LLM-As-A-Judge validation techniques

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Emerging

This project helps evaluate how well your Generative AI applications, especially those using Retrieval-Augmented Generation (RAG), are performing. It takes in your AI model outputs and validation datasets, then provides scores and insights into the quality of responses. This is for AI developers, machine learning engineers, and data scientists who build and refine AI systems.

Use this if you are building an AI application and need to systematically test and improve its accuracy, relevance, and overall effectiveness before deployment.

Not ideal if you are looking for a plug-and-play solution for end-user AI evaluation without needing to delve into code or specific model configurations.

AI-development LLM-evaluation RAG-testing Generative-AI-quality machine-learning-engineering
No Package No Dependents
Maintenance 6 / 25
Adoption 5 / 25
Maturity 16 / 25
Community 12 / 25

How are scores calculated?

Stars

11

Forks

2

Language

Jupyter Notebook

License

MIT-0

Last pushed

Oct 24, 2025

Commits (30d)

0

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