LLM Evaluation Result Segmentation by Event Property
PostHog users running LLM evaluations can only see aggregate pass/fail rates across all events. They cannot segment results to understand which prompt variants, model configurations, or environments are underperforming. This feature gap limits debugging capabilities for AI engineers building production LLM features.
Problem Statement
Users running LLM evaluations in PostHog currently see only aggregate metrics (pass rate, runs count, failing count) across all matching `$ai_generation` events. There is no way to filter or segment these results by event properties like `$ai_environment`, model name, or prompt version. This makes it difficult to identify which specific conditions are causing failures.
The Idea
A segmentation feature for AI engineers who need to understand LLM eval performance breakdown by event properties like model, environment, or prompt variant.
Why Now
AI observability is a rapidly growing category, PostHog has already launched LLM evals feature, and users are actively requesting the ability to slice evaluation results. The timing aligns with broader adoption of LLM evaluation practices in production.
Target User
AI engineers, ML engineers, and developers building and monitoring LLM-powered features in production
Target Market
AI/ML observability and LLM evaluation tooling
The full brief is free to read
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- Monetization model & pricing angle
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