🚀 Open-source RAG evaluation and testing with Evidently. New release

RAG testing and evaluation

Test, debug, and optimize your RAG pipelines to improve quality, reduce hallucinations, and deliver trusted AI outcomes.

test every step

From input to result

Ensure your retrieval-augmented generation (RAG) system retrieves the right data — and uses it correctly.

Our platform automates and scales RAG evaluation, helping you generate test data and run quality checks to get reliable, fact-based answers in production.

Evidently AI Testing for LLM

tests

Generate synthetic data

Automatically create test cases from your internal data sources to evaluate retrieval accuracy.

Structured test sets. Extract from documents, wikis, and databases.

Collaborate with experts. Refine test cases with domain specialists.

Comprehensive coverage. Simulate real-world queries, edge cases, and ambiguous inputs.

Evidently AI Evaluation for LLM-based systems

evals

Evaluate responses

Use built-in open-source RAG metrics or define your own.

Faithfulness. Are responses grounded in context?

Relevance. Is the retrieved data correct and accurate?

Completeness. Does the AI fully answer the query?

Evidently AI Testing for LLM

platform

Debug and optimize

Pinpoint retrieval or generation failures before they impact users.

Trace errors. Catch incorrect lookups, missing facts, and hallucinations.

Compare strategies. Test variations and analyze performance shifts across prompts.

Monitor quality. Track real-world performance and detect quality drift.

Start testing your AI systems today

Book a personalized 1:1 demo with our team or sign up for a free account.

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