# Evals Are the Product Spec

**Track:** Building the AI-Native Product — AI for Entrepreneurship — complete (29)
**Framework / surface:** venture strategy
**Level:** Intermediate
**Prerequisites:** The AI-Native Stack

**In one line:** The eval suite is the spec, the regression test, and the roadmap in one artifact — built from your job, not borrowed benchmarks.

## Theory, aesthetics & inspiration

In AI-native development the evaluation suite quietly absorbs three jobs that used to be separate documents: it is the specification (what does good output mean, made executable), the regression test (did the new model or prompt break what worked), and the roadmap (the failing evals are the backlog, ranked by customer pain). Stanford's HELM project (2022) demonstrated the form at research scale — holistic, multi-metric, scenario-based measurement — but the entrepreneurial translation cuts against borrowing: public benchmarks saturate, drift, and leak into training data, so a score on someone else's test says little about your customer's job. Build evals from the job you are hired for — real user failures, held-out cases the team cannot overfit — because a capability you have not written an eval for is a capability you do not actually manage. Evals are also what make the exponential navigable: "the new model dropped — are we better or broken?" becomes an afternoon's run, and teams with strong evals ship the upgrade while competitors are still collecting anecdotes.

**Founder question:** How would you know this output was actually good?