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

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

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
Mind Hard Samples for Fine-Tuning & Debugging

Mind 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

Continuously Validate Your
LLM Apps

Start early: integrate quality assessment during
development.

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

Config-Driven Development for LLMs: Versioning, Routing, & Evaluating LLMs
Config-Driven Development for LLMs: Versioning, Routing, & Evaluating LLMs
Fine-tuning LLMs with Hugging Face SFT 🤗
Fine-tuning LLMs with Hugging Face SFT 🤗
The Science of LLM Benchmarks: Methods, Metrics, and Meanings
The Science of LLM Benchmarks: Methods, Metrics, and Meanings 🚀

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