Основи на оценката на AI

Write the acceptance criteria first. Specify the input, expected output, tolerable errors, response-time constraints, and conditions that should cause the system to abstain or escalate. Include a simple baseline to show whether added complexity provides a practical benefit. Build separate development and evaluation sets. Development examples support iteration; a held-out set tests choices after they are made. Repeatedly tuning on the final test set turns it into another development set. Record versions so a changed score can be traced to changed data, prompts, models, or scoring. Use metrics appropriate to the task. A classifier needs class-specific error analysis; a summarizer needs checks of factual consistency and coverage; an agent needs verification of completed actions and unintended side effects. Include difficult cases rather than only typical inputs. Review results with uncertainty and consequences in mind. A rare failure may matter more than many harmless wording differences. Repeat a stochastic task enough to understand variation, and document where the evaluation does not represent actual use. Evaluation supports a decision; it does not eliminate uncertainty.