Evals Are Not Unit Tests — Ido Pesok, Vercel v0

Key Takeaways on LLM Evaluation - LLMs can be unreliable, impacting user experience and application usability [6] - AI applications are prone to failure in production despite successful demos [7] - It is crucial to build reliable software using LLMs through methods like prompt engineering [8] Evaluation Strategies and Best Practices - Evals should focus on relevant user queries and avoid out-of-bounds scenarios [19] - Data collection methods include thumbs up/down feedback, log analysis, and community forums [21][22][23] - Evals should test across the entire data distribution to understand system performance [20][24] - Constants should be factored into data, and variables into tasks for clarity and reuse [25][26] - Evaluation scores should be deterministic and simple for easier debugging and team collaboration [29][30] - Evals should be integrated into CI pipelines to detect improvements and regressions [34][35] Vercel's Perspective - Vercel's Vzero is a full-stack web coding platform designed for rapid prototyping and building [1] - Vzero recently launched GitHub sync, enabling code push and pull directly from the platform [2] - Vercel emphasizes the importance of continuous evaluation to improve AI app reliability and quality [37] - Vercel has reached 100 million messages sent on Vzero [2]