Deepchecks LLM Evaluation
For Summarization

Balance quality, scalability, and fairness throughout the development lifecycle.
Make Sure Your LLM Application Acts as Expected

Make Sure Your LLM
Application Acts as
Expected

Use various auto-calculate properties to mitigate hallucinations, and improve the performance of your LLM app across dimensions like accuracy, conscience, and sentiment.

Test different:

  • Coherence, Conciseness & Coverage to improve the overall performance
  • Grounded in Context detects hallucinations
  • Toxicity and Sentiment to keep your users safe

Choose any number of off-the-shelf properties and add your own custom properties.

Iterate with Confidence

Iterate with Confidence

Avoid surprises in production by comparing your experiments and versions.

  • Different LLMs
  • Prompt versions and experiments
  • LLM configuration
Automated Evaluation with Manual Override

Automated Evaluation with
Manual Override

  • Have the human-in-the-loop where necessary
  • Use pre-built auto-annotation pipeline
  • Fine-tune auto-annotation to your specifications
Monitor Your LLM-Powered App in Production

Monitor Your LLM-
Powered App in Production

LLM applications require much more than just
input and output format validation.

Hallucinations, harmful content, model
performance degradation, or a broken data
pipeline are common problems that may arise
over time.

Apply rigorous checks to ensure your LLMs
consistently deliver optimal performance.

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LLMOps Past Events

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The Science of LLM Benchmarks: Methods, Metrics, and Meanings
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