Run an AI feature eval with golden dataset

AI Evals: From Demos to Defensible Numbers

"Looks good to me" is not an eval — and it collapses the first time a stakeholder asks for a number. Anthropic's eval research and GitHub's developer productivity writing both document the required rigor: real production inputs, expert ground truth, blind scoring, statistical significance. LLM-as-judge can scale evaluation — but only when calibrated against human labels first.

How the Run an AI feature eval with golden dataset Prompt Works

The prompt builds a balanced dataset with held-out split, runs three scoring mechanisms (automatic metrics, LLM-judge, human scoring), and applies statistical rigor (confidence intervals, sample size, multiple-comparison correction). The reporting template produces numbers defensible in front of a skeptical stakeholder.

When to Use It

• Leadership is asking for an AI quality number.
• A model migration needs objective comparison.
• A regulated industry requires documented eval procedures.
• A compliance or procurement review requires defensible metrics.
• A new AI PM is establishing eval discipline.

Common Pitfalls

• Dataset curated by PM, not experts. PM-curated datasets bias toward cases PMs think matter — not cases users encounter.
• Non-blind scoring. Scorers who know which model is the new one rate it higher. Blind always.
• Single-number reporting. An overall score without per-category breakdown hides unreliable categories.

Sources

• Anthropic Research — Anthropic
• GitHub Developer Research — GitHub
• AI Adoption in Product Orgs — Reforge
• Stack Overflow Blog — Stack Overflow