Evaluation for LLM-Based Apps | Deepchecks

Data Stack
DS/ML Tooling
Status
Paying Customers
Summary

Deepchecks offers LLM Evaluation, a comprehensive solution to evaluate the quality and compliance of LLM apps. With automated evaluation processes, Deepchecks helps detect and mitigate issues such as incorrect answers, bias, hallucinations, and harmful content. Join thousands of practitioners in the LLMOps.Space community. Learn more

Who's using?
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LLM Evaluation

Release high-quality LLM apps quickly without compromising on testing. Never be held back by the

complex and subjective nature of LLM interactions.

Try LLM Evaluation

Evaluation is Complex

Generative AI produces subjective results. Knowing whether a generated text is good usually requires

manual labor by a subject matter expert.

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Evaluation is Complex

A small change in the answer might change the meaning of the answer completely.

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Evaluate Quality & Compliance

Evaluate quality & compliance

If you’re working on an LLM app, you probably

know that you can’t release it without addressing

countless constraints and edge-cases.

Hallucinations, incorrect answers, bias, deviation

from policy, harmful content and more need to be

detected, explored and mitigated before and

after your app is live.

Deepchecks does it systematically.

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Golden Set

A proper Golden Set (The equivalent of a test set for GenAI)

will have at least a hundred examples. Manual annotations

typically take 2-5 minutes per sample, and require waiting,

reviewing, correcting and sometimes hiring.

Good luck with doing this for every experiment or version

candidate!

Deepchecks’ solution enables you to automate the

evaluation process, getting “estimated annotations” that

you only override when you have to.

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Open Core Product

DEEPCHECKS LLM EVALUATION IS BASED ON THE LEADING ML OPEN SOURCE TESTING PACKAGE

Used by 1000+ companies, integrated into 300+ open source projects, the core behind our LLM

product is widely tested and robust.

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Open Core Product

Open Source ML Testing

Deepchecks Open Source is a Python-based

solution for comprehensively validating your

machine learning models and data with minimal

effort, in both the research and the production

phases.

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Open Source ML Testing

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ML Monitoring

ML Monitoring

Model performance is a critical component of a

healthy application. To maximize your business

performance, ML and IT teams need to

continuously know the status of their model.

Deepchecks Monitoring makes sure that your

models and data are validated continuously.

3.2KTry LLM Evaluation

LLMOps.Space

Deepchecks is a founding member of LLMOps.Space, a global community for LLM

practitioners. The community focuses on LLMOps-related content, discussions, and

events. Join thousands of practitioners on our Discord.

Join Discord Server