Stop AI slop: Run evals with LLM-as-a-Judge

Evaluations are essential for assessing the performance and reliability of AI products, particularly those powered by large language models (LLMs), as they are frequently judged by users based on the quality of outputs which can influence trust and retention. PostHog offers an evaluation system that leverages LLMs to automatically score AI outputs against criteria like relevance, helpfulness, and toxicity, allowing for the identification of "AI slop"—low-quality or incorrect outputs. The system includes pre-built templates for various evaluation scenarios and allows for custom evaluations tailored to specific use cases. These evaluations help scale the review process, mitigate brand risks, and improve AI product quality by connecting results to user behavior and business metrics. They serve as unit tests for AI products, providing insights into user interactions and facilitating data-driven improvements to enhance user retention and satisfaction.

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