LLM evaluation guide: When to add online evals to your AI application
This article provides a framework for deciding when to add online evaluations to AI applications using LLM-as-a-judge methodology. It explains the benefits of online evals over manual quality review and how they work with LaunchDarkly's built-in judges.
Why it matters
Online evals help scale quality monitoring for high-volume LLM applications, enabling automated quality gates and actions.
Key Points
- 1Online evals provide real-time quality monitoring for LLM applications
- 2They automatically assess quality across accuracy, relevance, and toxicity
- 3Online evals are useful when manual review doesn't scale, you need to trigger automated actions, or you're running A/B tests
- 4LaunchDarkly's online evals use LLM-as-a-judge methodology with built-in judges that can be configured in the dashboard
Details
The article outlines a decision framework for when to add online evaluations to AI applications. Online evals use LLM-as-a-judge methodology to automatically score a sample of production traffic across quality dimensions like accuracy, relevance, and toxicity. This provides real-time quality monitoring and the ability to trigger automated actions like rollbacks or rerouting based on quality thresholds. The article contrasts online evals with LLM observability, explaining that evals automatically assess quality while observability shows what happened for debugging. LaunchDarkly's online evals include three built-in judges that can be configured in the dashboard without code changes.
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