Ragas Documentation
by Ragas
Replace vibe checks on your RAG pipeline with faithfulness, answer relevancy and context precision scores you can regression-test.
Overview
Ragas describes itself as the library that moves teams from 'vibe checks' to systematic evaluation loops for AI applications. Its core is a set of LLM-driven metrics that score a RAG pipeline on the axes that matter: faithfulness (does the generated answer stay grounded in the retrieved context), answer relevancy (does it actually address the query), context precision (how much of what was retrieved was useful) and context recall (how much of the needed information was retrieved at all). Beyond the built-in metrics, the docs cover writing custom metrics, synthetic test-set generation so you can build an evaluation dataset without hand-labeling everything, evaluation of tool-using agents as well as plain RAG, a CLI and experiment workflow for tracking runs over time, and integrations with LangChain, LlamaIndex and the major LLM providers. Because the metrics decompose a failure into a retrieval problem versus a generation problem, Ragas is most useful as a diagnostic loop: run it against a fixed dataset, change one component of the pipeline, and see which score moves.
At a Glance
- Topic
- RAG
- Level
- Intermediate
- Format
- Documentation
- Cost
- Free
- Duration
- ~2-4 hours, self-paced
- Provider
- Ragas
- Hands-on
- Yes — code/exercises
- Certificate
- None
What You’ll Learn
- ✓How to evaluate a RAG pipeline end to end with faithfulness, answer relevancy, context precision and context recall
- ✓How to read those metrics diagnostically — telling a retrieval failure apart from a generation failure
- ✓How to generate a synthetic test set from your own documents instead of hand-labeling evaluation data
- ✓How to define custom LLM-as-a-judge metrics for domain-specific correctness criteria
- ✓How to evaluate tool-calling agents, not just question-answering over documents
- ✓How to wire Ragas into LangChain or LlamaIndex and run evaluations as a repeatable experiment
Highlights
- •The de facto open-source standard for RAG evaluation metrics — cited and reimplemented across the eval ecosystem
- •Synthetic test-set generation removes the biggest blocker to starting an eval loop
- •Covers agent evaluation, not just classic retrieve-then-generate
- •Free and open source, with first-party LangChain and LlamaIndex integrations
Who It’s For
Best For
- ✓AI engineers shipping a RAG system who need to prove it improved
- ✓Teams building an evaluation harness and CI regression gates for LLM apps
- ✓Anyone debugging whether bad answers come from bad retrieval or bad generation
Prerequisites
- •Python
- •A working RAG pipeline or at least familiarity with embeddings, vector search and prompting
- •An LLM API key to run the judge models
FAQ
What is Ragas Documentation?
The official documentation for Ragas, the open-source evaluation library for RAG and agent applications. It is for AI engineers who have a retrieval pipeline working and now need a systematic, reproducible way to measure whether retrieval and generation are actually correct.
Is Ragas Documentation free?
Ragas Documentation is free to access.
What level is Ragas Documentation for?
Ragas Documentation is aimed at a intermediate audience. Recommended background: Python, A working RAG pipeline or at least familiarity with embeddings, vector search and prompting, An LLM API key to run the judge models.
How long does Ragas Documentation take?
Expect roughly ~2-4 hours, self-paced. Most learners work through it at their own pace.
What will I learn from Ragas Documentation?
You'll learn: How to evaluate a RAG pipeline end to end with faithfulness, answer relevancy, context precision and context recall; How to read those metrics diagnostically — telling a retrieval failure apart from a generation failure; How to generate a synthetic test set from your own documents instead of hand-labeling evaluation data; How to define custom LLM-as-a-judge metrics for domain-specific correctness criteria; How to evaluate tool-calling agents, not just question-answering over documents; How to wire Ragas into LangChain or LlamaIndex and run evaluations as a repeatable experiment.