LLM evals + Hacktoberfest = ❤️ Learn how to contribute new LLM evaluation metrics to the open-source Evidently library

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.
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.
Evidently AI Testing for LLM
Test

Catch regressions

Run systematic tests for key scenarios. Make sure updates don't cause issues and detect prompt drift in new model versions.
Evidently AI Monitoring for LLM
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. 
Evidently AI Debugging for LLM
Debug

Find bad responses

See where exactly your models struggle. Discover clusters of similar issues to prioritize fixes. 
Collaborative AI Observability platform
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.
Icon
Assertions
Apply rules, functions, and regex to reliably and quickly test at scale.
Icon
Classifiers
Score outputs by topic, sentiment, and more using ML models.
Icon
LLM as a judge
Assess complex behavior with LLMs. Use templates or bring your prompts.
Icon
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:
Icon
Trigger Words
Detect unwanted words or phrases.
Icon
Toxicity
Identify offensive or discriminatory language.
Icon
Hallucinations
Find outputs that are factually wrong or out of context.
Icon
Retrieval Quality
Assess whether the retrieved content is relevant.
Icon
Output Length
Verify the expected word or character range.
Icon
PII Detection
Check if queries or outputs include personal data.
Icon
Answer Relevancy
Measure how well the response addresses the query.
Icon
Accuracy
Evaluate the share of correct classifications.
Icon
Format Compliance
Track that generated outputs fit the requested format. 
Icon
Intent Classification
Understand the purpose behind the user's query.
Icon
Prompt Injection
Catch attempts to manipulate the model.
Icon
Semantic Similarity
Compare how well the response aligns with the reference.
See documentation
Icon

Get Started with AI Observability

Book a personalized 1:1 demo with our team or sign up for a free account.
Icon
No credit card required
By clicking “Accept”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts. View our Privacy Policy for more information.