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LLM evaluation and observability

Create reliable AI products from the first trial to scale. Automate evaluation for RAGs, chatbots, and AI agents. Detect errors before they impact users.

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Evidently AI Evaluation for LLM-based systems

Evaluate

Get a quality breakdown

Easily visualize performance for different prompts, models, or segments. See what works and fix what doesn't.

Test

Catch regressions

Run systematic tests for key scenarios. Make sure updates don't cause issues and detect prompt drift in new model versions.

Monitor

Run checks on live data

Know what users want and how your product performs on real traffic. Get alerts if things don’t go as expected.

Debug

Find bad responses

See where exactly your models struggle. Discover clusters of similar issues to prioritize fixes.

Collaborate

Work as a team

Bring product and engineering to one workspace. Run the same evaluations in code or UI. Share results with custom charts.

Toolset

Define AI quality on your terms

Use built-in checks and a toolbox of methods to craft your evaluation framework.

Assertions

Apply rules, functions, and regex to reliably and quickly test at scale.

Classifiers

Score outputs by topic, sentiment, and more using ML models.

LLM as a judge

Assess complex behavior with LLMs. Use templates or bring your prompts.

Metrics

Track task-specific metrics for ranking, classification, or summarization.

Evals

From simple checks to nuanced behavior

To tweak prompts or keep an eye on production, here are some things you can track:

Trigger Words

Detect unwanted words or phrases.

Toxicity

Identify offensive or discriminatory language.

Hallucinations

Find outputs that are factually wrong or out of context.

Retrieval Quality

Assess whether the retrieved content is relevant.

Output Length

Verify the expected word or character range.

PII Detection

Check if queries or outputs include personal data.

Answer Relevancy

Measure how well the response addresses the query.

Accuracy

Evaluate the share of correct classifications.

Format Compliance

Track that generated outputs fit the requested format.

Intent Classification

Understand the purpose behind the user's query.

Prompt Injection

Catch attempts to manipulate the model.

Semantic Similarity

Compare how well the response aligns with the reference.

See documentation

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