Build your first AI eval in a weekend

Mine your production logs, support tickets, or your own usage for real inputs. Include the messy ones: ambiguous questions, multi-part requests, edge cases, and past failures. Twenty real prompts beat two hundred synthetic ones, because realism is what makes the score meaningful.

For each case, write down what a good answer must do. Be specific: "cites the correct policy section," "returns valid JSON," "refuses politely." Vague criteria like "is helpful" produce noisy scores. If a human can't apply your rubric consistently, neither can a judge model.

Run each prompt through your pipeline and grade the outputs. Start by hand — grading 20 outputs yourself calibrates your judgment. Then automate: exact match where answers are verifiable, an LLM-as-judge prompt where they're not. Check that the automated grader agrees with your hand grades on at least 80% of cases before trusting it.

Record every run: date, model, prompt version, score. A spreadsheet is fine. The goal isn't a dashboard — it's a number that moves when your product gets better or worse.

Re-run the eval before every prompt change, model swap, or release. When the score drops, the deploy waits. Congratulations: you now have what most teams don't — evidence.