model
Eval-driven AI development
An AI product should define how success will be evaluated before the team invests in deeper prompt, model, or workflow work.
正文
Eval-driven AI development treats an AI feature as a behavior to be tested, not just a demo to be admired. The team describes the task, runs it against representative inputs, studies where it succeeds or fails, and then changes the prompt, workflow, model, or product scope.
For early ideas, this converts subjective enthusiasm into an iteration loop. A Brain Dice idea can move from a concept into a small eval plan before it becomes a build commitment.
来源引用
Working with evals
Source: Working with evals
OpenAI frames eval work as describing the task, running test inputs, analyzing results, and iterating.
Evaluation best practices
Source: Evaluation best practices
OpenAI recommends evaluating early and often, with scoped tests at each stage.
相关卡片
Validation brief
A validation brief compresses an idea into target user, problem, proof needed, and the smallest useful test.
AI-assisted iteration cycle
AI-assisted iteration works best when generation is paired with feedback, scoring, and reusable learning.
Task-specific eval objective
A useful AI eval starts with a task-specific objective that names what the system must do well in its real product context.
所在阅读路径
AI idea validation to eval
A source-backed path for turning generated AI product ideas into problem framing, validation briefs, task-specific evals, and score-gated decisions.