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Ragas

Evaluation framework for LLM applications, especially RAG systems and systematic experimentation loops.

Resource overview Use to move AI applications from vibe checks to repeatable evaluation using metrics, experiments, datasets, and integration with common LLM frameworks.

Why use this

What it helps you do

Use this to move AI work from manual judgment to repeatable testing, tracing, metrics, prompt experiments, and quality improvement loops.

Who can use this

Best fit users

AI engineers, QA teams, platform teams, and product teams responsible for reliability, regression testing, and measurable AI quality.

Prerequisites

What to know first

Representative test cases, logs or traces, target quality metrics, and enough production context to know what good and bad outputs look like.

System requirements

Environment needed

Python environment for most workflows, access to model calls or traces, datasets for evaluation, and storage for experiment results when running repeatedly.

Community

Signals and discussion

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