OpenLIT provides automated evaluation that helps you assess and monitor the quality, safety, and performance of your LLM outputs across development and production environments.

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Why evaluations?

Evaluation is crucial for improving the accuracy and robustness of language models, ultimately enhancing user experience and trust in your AI applications. Here are the key benefits:
  • Quality & Safety Assurance: Detect hallucinations, bias, toxicity, and ensure consistent, reliable AI outputs
  • Performance Monitoring: Track model performance degradation and measure response quality across different scenarios
  • Risk Mitigation: Catch potential issues before they reach users and ensure compliance with safety standards
  • Cost Optimization: Monitor cost-effectiveness and ROI of different AI configurations and model choices
  • Continuous Improvement: Build data-driven insights for A/B testing, optimization, and iterative development

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AI evaluation methods

OpenLIT provides automated LLM evaluation and testing capabilities for production AI applications:

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Automated LLM-as-a-Judge

Zero-setup AI quality monitoring that automatically evaluates your LLM responses:
  • Production Monitoring: Auto-evaluate every LLM response for quality and safety issues
  • Smart Scheduling: Configure evaluation frequency and sampling for cost optimization
  • Real-time Scoring: Instant evaluation results visible in trace details and dashboards

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Programmatic evaluations

Programmatic AI evaluation tools for custom testing workflows and development pipelines:
  • Code Integration: Call LLM evaluators directly in your application code
  • CI/CD Quality Gates: Automated testing for model improvements and regression detection

LLM-as-a-Judge

Use advanced LLMs to evaluate AI application quality with automated scoring

Programmatic evaluations

Quick start guide for implementing custom evaluations in your code

Create a dashboard

Create custom visualizations with flexible widgets, queries, and real-time AI monitoring