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Evaluate, test, and monitor NLP and LLM-powered systems

Know what inputs your models get and how they respond. Make sense of unstructured data to uncover patterns and changes.

Evidently NLP and LLM monitoring
What is LLM and NLP monitoring?

LLMs and NLP models can produce unexpected or incorrect responses, and their quality may decline over time due to data drift. To ensure reliable operation, understanding their behavior is crucial. This can be challenging as there is often no labeled data, and it's hard to analyze large text datasets.

How Evidently helps

Evidently extracts interpretable signals from unstructured data, giving a clear view of model inputs, outputs, and how they change. Know when to label or fine-tune the models, modify prompts, and what behaviors require attention.

Run text-based models with confidence

Structure the unstructured

Capture important properties of text data with auto-generated descriptors: from the number of words to text sentiment. Track them over time to detect shifts.

Evidently for text data
Evidently for detecting drift in text data

Detect text data drift

Know if the new data is unlike the old one. Identify the specific words that contributed the most to drift detection results. 

Monitor embeddings

Catch changes in embedding distributions. Pick and tune methods, from distance metrics to model-based drift detection.

Evidently for monitoring ML embeddings
Evidently for regular expression checks

Test for matches

Check if model inputs or outputs match a regular expression or contain specific words. Track properties over time and monitor compliance.

Monitor model quality

Evaluate prediction drift to know if things have changed, and it's time to label your data. Quickly visualize model quality whenever you get feedback. Track everything.

Evidently for monitoring ML model quality

Yes, it's open-source!

With a lot of examples in the docs.


Get started

Easily add Evidently to existing workflows, no matter where you deploy. 


Integrate in minutes and self-host.
Get full privacy and control.



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