Why evals are essential for AI product managers

AI product teams often face challenges with maintaining quality due to the probabilistic nature of AI outputs, which differ from traditional deterministic product features. This can lead to "AI slop," where products perform well internally but fail to satisfy users. The solution lies in implementing evaluations or "evals," which focus on assessing the quality of outputs based on criteria like accuracy and relevance rather than just system functionality. This process involves systematically reviewing outputs, identifying failure patterns, and iterating on solutions. A case study with an AI-powered news aggregator, BITS, demonstrates how evals help pinpoint issues within a multi-stage processing pipeline by tracing problems back to their origins. Evals are not just a quality assurance tool but also a driver for product development, as they provide actionable insights that guide improvements. The process can begin manually with simple tools and evolve to include automation as the evaluation framework matures. Ultimately, evals ensure that AI products meet user expectations and maintain high quality by replacing guesswork with data-driven decisions.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.