Deepchecks LLM Evaluation
for Text Generation

Apply multi-faceted evaluation for Your LLM Text Generation Application.

Text generation is one of the best LLM use cases
and provides excellent value, but it also has
quality and safety risks.

Bias

Sentiment

Inconsistency

Hallucinations

Toxicity

Bias

Sentiment

Inconsistency

Hallucinations

Toxicity

Hard Wire Reproducibility
into Your LLM App

Rigorously check your application using
Deepchecks’s off-the-shelf properties and
couple it with your own custom metrics.

Strike the Right Balance
Between Manual and Fully
Automated Evaluations

Have the human-in-the-loop where necessary
Use pre-built auto-annotation pipeline
Fine-tune auto-annotation to your specifications

Try LLM Evaluation

Mine Hard Samples
for Fine-Tuning & Debugging

Handle rare or unseen scenarios where your LLM
application is not performing well. Use it to
modify the code or prompt, or just add it to the
golden set for the next iteration of re-training.

Continuously Validate Your
LLM Apps

Start early: integrate quality assessment during
development.

Try LLM Evaluation

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