Eval-driven development: Build better AI faster

This post introduces and advocates for 'eval-driven development' as a new paradigm for building AI systems, contrasting it with traditional deterministic testing. It details three types of evals (code-based, human, LLM-based) and explains their necessity due to the probabilistic nature of AI. The post uses an example of generating a React component to illustrate how evals assess not just functionality but also code quality and style adherence. It then presents the 'AI-native flywheel' concept, where evals, data, models/strategies, and feedback form a continuous improvement cycle. Vercel's v0 product is highlighted as a practical application of this methodology, emphasizing the use of evals for rapid iteration, regression prevention, and ensuring quality in areas like refusal and safety. The post also touches on the role of the AI SDK in simplifying model switching and the use of tools like Braintrust for automated evaluations.