How to Know If Your AI Feature Is Ready to Ship
For Startup founders shipping their first AI feature · Based on Hetzel Eval Maturity Phases Framework
// TL;DR
If you're a startup founder shipping your first AI feature, the Hetzel Eval Maturity Phases Framework helps you prove quality without over-engineering. You don't need a huge eval system on day one — vibe checking with documented human annotation is a legitimate starting point. Have a domain expert review 10–20 real outputs with written justifications, derive your actual failure modes, and build a few targeted scoring functions. As you grow, add production traces and the flywheel. Use it to bridge from 'the demo works' to 'we can defend this in production.'
How much eval do you actually need to ship?
Less than you fear, but more than a demo. As a founder, your instinct is to ship fast, but shipping an AI feature that hallucinates, leaks data, or burns cost creates reputational and compliance risk. The Hetzel Eval Maturity Phases Framework lets you start small and mature deliberately. The most important message: waiting until everything is perfect before starting evals is a mistake. Vibe checking with documented human annotation is better than nothing and is a legitimate starting point.
What's the cheapest first eval you can run?
Run a Level 1 vibe check — but do it properly. Take 10–20 real inputs your users would actually send, run them through your feature, and have someone with domain knowledge review each output. For every output, record a thumbs up or down and, crucially, a written justification explaining why. The justification is the asset. It captures what 'good' means for your product in a way you can reuse later.
If you're a solo founder, you may be the domain expert — but be honest about whether you're too close to the build to judge objectively. Where possible, get a real subject matter expert.
How do you avoid over-engineering evals as a small team?
Remember that evals are not unit tests. You do not need to cover every possible failure. Start with the known, high-priority failure modes for your specific feature and build evals only around those. Trying to be exhaustive means you'll spend all your time on evals and none shipping — fatal for a startup. Also, don't chase perfect scores. Directional improvement is the goal; if scores trend up as you iterate, you're winning.
When should you add more sophistication?
Add sophistication when the pain appears, not before. Once manual review can't keep up, feed your thumbs-down justifications into a coding assistant to extract failure modes, then build a few scoring functions — code-based for deterministic issues like cost or format, LLM-as-judge for subjective ones. If you use an LLM-as-judge, eval it against a small human-labelled set first so you don't build false confidence.
When you have real usage, swap synthetic test inputs for real production or UAT traces. This makes your evals reflect what actually happens rather than what you imagined would happen.
How does this help you raise money or close enterprise deals?
Investors and enterprise buyers increasingly ask how you ensure AI quality. Being able to say 'we run structured evals against real production traces, we validate our LLM judges against human ground truth, and we have a flywheel turning production failures into improvements' is a credible, defensible answer. It signals engineering maturity far beyond 'the demo works.'
Next step
Before you ship your AI feature, run one Level 1 annotation session this week: 15 real inputs, a domain expert recording verdicts and written justifications for each. Those justifications are the seed of everything else — keep them, and revisit the framework as you grow.
// FREQUENTLY ASKED QUESTIONS
Can I ship my AI feature with just vibe checking?
You can start there, as long as your vibe checking includes documented human annotation — verdicts plus written justifications. Vibe checking with documented annotation is a legitimate starting point and is far better than nothing. Just don't wait for a perfect eval system before shipping, and plan to mature toward scoring functions and production traces as usage grows.
I'm a solo founder — can I be my own domain expert?
Sometimes, but be cautious. You may be too close to the build to judge outputs objectively. The framework recommends the reviewer be a subject matter expert rather than the builder. If you can't bring in a separate expert, at least review with fresh eyes on a different day and document your justifications rigorously.
How do I avoid spending too much time on evals as a startup?
Treat evals as not unit tests. Target only the known, high-priority failure modes for your feature instead of exhaustive coverage, and don't chase perfect scores — directional improvement is enough. Start with a small annotation session and a few scoring functions, then add sophistication only when manual review genuinely can't keep up.