Design an AI feature evaluation rubric before shipping

AI Features Without Evals Are Untested Code in Production

Shipping an AI feature without a structured eval is equivalent to shipping code without tests — and the failure modes are harder to catch because the output looks plausibly correct. Anthropic's research writing and PostHog's AI analytics writing both document the eval pattern: a task set spanning happy path, edge cases, and adversarial inputs, scored across 3-5 dimensions with golden-answer comparison. Evals catch 60-80% of regressions before users see them.

How the Design an AI feature evaluation rubric before shipping Prompt Works

The prompt builds a task set across four input categories, defines a multi-dimensional rubric with golden answers, and sets regression guardrails with automated diffing. The "failure mode we'd tolerate" output is the honest tradeoff — perfect AI is unshippable, and naming the tolerated failure makes the tradeoff intentional.

When to Use It

• An AI feature is approaching ship and the eval plan is thin.
• A model upgrade is being considered and regression risk is unknown.
• A previous launch produced hallucinations that the team missed.
• A new AI PM is establishing eval discipline.
• A board is asking how AI quality is measured.

Common Pitfalls

• Happy-path-only test set. If your evals only cover the cases the team is proud of, production will surface everything you missed.
• Single-dimension scoring. Correctness alone misses safety, faithfulness, and completeness. Score on all.
• No regression automation. Manual evals degrade. Automate the diff on every model/prompt change.

Sources

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