Your AI Feature Has No Tests, Just Vibes

Ask a founder how they know their AI feature works and you'll usually get the same answer: "I tried it a few times and it seemed fine." That's not testing. That's vibes.

Nobody would accept that for a payment flow. You'd want unit tests, integration tests, maybe a staging environment where you hammer on edge cases before real money touches it. But the summarizer, the support bot, the AI that drafts emails or classifies tickets? Most teams ship it, glance at ten outputs, and move on. Then three weeks later a customer gets a nonsense answer, or the model provider quietly updates something upstream, and nobody notices until support tickets pile up.

Why this happens

LLM output feels fuzzy, so people treat it like it can't be tested the way regular code can. That's backwards. You can't assert on exact string equality, sure, but you can absolutely define what "good" looks like and check for it automatically:

• Does the output contain the required fields (a price, a date, a name)?
• Does it avoid saying things it's explicitly not allowed to say?
• Does it stay under a length limit, or match a tone your brand actually wants?
• Does a sample of outputs, scored by a second model or a human rubric, clear a quality bar you set on purpose?

That's an eval. It doesn't need to be fancy. A spreadsheet of twenty real inputs with expected properties, checked automatically on every prompt or model change, beats "I tried it a few times" every single time.

The part that actually costs you money

Without evals, you find out your AI feature broke the same way you find out your website is down: a customer tells you, annoyed, after the fact. Model providers change default behavior. Prompts drift as people "just tweak one line." Someone adds a new instruction that quietly breaks an old one. None of this shows up in a demo. All of it shows up in production, weeks later, as a support ticket or a churned customer who never says why.

The fix isn't complicated, it's just skipped. Write down what correct output looks like. Build a small, boring test set. Run it before every change ships, not after a customer complains.

Where Cedrix fits

This is the kind of gap we see constantly when we come in to build or fix an AI feature for a client: the product works in the demo and nobody knows if it still works six weeks later. If you're shipping (or planning to ship) an AI feature and want it built with real evaluation baked in from day one, our free MVP offer is a good place to start. And if you already have something live that you suspect is quietly degrading, get in touch and we'll help you find out for sure.