Glossary · AI & Agents
Ragas
Overview
What Ragas Is
Ragas is an open-source Python framework for evaluating retrieval-augmented generation systems and other LLM applications. Instead of eyeballing whether a chatbot's answers seem good, Ragas produces objective scores on defined metrics, such as faithfulness (is the answer grounded in the retrieved context), answer relevancy, context precision, and context recall. Many metrics use an LLM as an automated judge.
Why Evaluation Matters
RAG systems can fail quietly: retrieving the wrong documents, or generating confident answers the sources never support. Without measurement, these problems surface only when a user complains. Ragas lets teams build a test set, score each change, and catch regressions before they ship. It turns 'the bot feels better' into numbers you can track and improve over time.
How We Use It in Delivery
When we build RAG chatbots and knowledge assistants, evaluation is part of the engineering loop, not an afterthought. We use frameworks like Ragas to benchmark retrieval and answer quality as we tune chunking, embeddings, and prompts. For AI consulting clients, these scores give a clear, defensible picture of how a system performs before and after each change.
Where we use it
Related Zen in Tech services
How our team puts Ragas to work in real projects.
FAQ
Ragas — common questions
Is Ragas free?
Yes. Ragas is open-source and free to use. The main cost is the LLM API calls it makes when running LLM-as-judge metrics, since evaluating a large test set consumes tokens.
Ragas vs TruLens?
Both evaluate RAG and LLM apps. Ragas focuses on a curated set of RAG metrics and batch evaluation, while TruLens emphasizes feedback functions plus tracing and observability for live apps. Teams sometimes use them side by side.
What does Ragas measure?
Ragas scores metrics like faithfulness (grounding in sources), answer relevancy, context precision, and context recall, so you can tell whether retrieval and generation are each doing their job.
Need Ragas done right?
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