Even (very) noisy LLM evaluators are useful for improving AI agents
Noisy LLM evaluators can still be useful for improving AI agents despite their limitations. While they may not be reliable for production decisions based on single outputs, they can help in selecting the best agent variants over time. The key insight is that the noise in evaluations averages out, allowing for better overall assessments of agent quality.
- ▪Noisy evaluators are often weakly correlated with real-world outcomes.
- ▪They can reliably indicate which agent is better on average, aiding in variant selection.
- ▪Output-level correlation measures are crucial for production workflows, while agent-level correlation helps in offline variant selection.
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Even (very) noisy LLM evaluators are useful for improving AI agents May 12, 2026 · Alan Mishler SummaryLLM evaluators are often noisy and weakly correlated with real-world outcomes.Noisy evaluators have limited value for production decisions that hinge on judging a single output (e.g. guardrails).However, even (very) noisy evaluators can reliably tell you which agent is better on average, meaning they can still help you pick the best variant to deploy and improve it over time. It’s surprisingly hard to develop reliable LLM evaluators: they’re often noisy and poorly correlated with the metrics or outcomes practitioners actually care about. Sometimes the target is directly measurable but evaluators still disagree with experts (e.g. on correctness or faithfulness to a source document).
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