Most seed-stage teams shipping AI features do not have evals. They have hope. They have a couple of dashboards. They have a Slack channel where customers report when the thing did something stupid. None of that is an eval.
This becomes a problem the moment your AI feature is doing something a customer relies on. Hope works fine when the feature is a demo. It does not work when somebody is making a decision based on the output. And nobody tells you which moment is which, so most teams find out by accident.
What an Eval Actually Is
An AI eval is just a test that asks “did the model produce a correct output for this specific input.” That is it. There is nothing mystical about it. A few dozen carefully chosen examples, run on every change to the prompt, model, retrieval setup, or fine-tune. A pass-fail signal or a graded score.
The reason teams skip evals is not because they are hard. It is because at seed stage there is always something more immediately valuable to ship, and evals look like infrastructure for a problem you do not have yet. They look like infrastructure until the day you change the model and the customer-facing accuracy quietly drops by twenty percent. By then it is no longer infrastructure for a problem you do not have. It is post-mortem material for a problem you already have.
The Five Evals I Tell Seed-Stage Teams to Build First
You do not need a full eval suite at seed. You need a starting set. Five of them, in this order.
1. A regression eval on the top 20 inputs. Pick the 20 inputs that represent what your customers actually run through the feature. Write down the correct output for each. Run them after every prompt change, model change, or retrieval change. If any of them break, find out why before you ship.
2. A failure-mode eval. Collect the cases where the AI feature did something wrong in production. Each one becomes a new test. Over time this is the eval that catches the most regressions, because the model loves to repeat the exact mistake you just fixed.
3. An adversarial eval. A small set of inputs designed to trip the model. Prompt injections, edge cases, malformed input, ambiguous requests. You are not trying to make the model handle them perfectly. You are trying to make sure the failure mode is graceful and not catastrophic.
4. A latency and cost eval. Not just accuracy. Track p50 and p95 latency for the same inputs and the cost per call. When you switch models or change prompts, you want to see if you are about to ten-x your bill or your wait time. This is the eval that catches the changes that look correct but are unshippable.
5. A judgment eval. For the cases where there is no single right answer. Have a stronger model, or a human for the small set, judge the output against criteria you wrote. This is the eval that catches “technically correct but useless” outputs.
How to Run Them at Seed Stage
You do not need a vendor. You do not need an MLOps platform. You need a script that loads the test cases, calls the model, compares the output, and prints a pass-fail summary. Forty lines of Python is enough.
Run the script on every change to the AI part of your codebase. Treat it the way you treat your regular tests. If the AI part does not have a script that produces a pass-fail signal, you do not have evals. You have hope.
Where the Real Breakage Happens
The thing that goes wrong in the second cluster of teams I see is not the absence of evals. It is the gap between when the team thinks the feature works and when it actually works in production.
Without evals, that gap is invisible. The team ships a prompt change on Wednesday. By Friday, three customers have noticed something weird. By Monday, a senior engineer has spent half a day finding the regression. By Tuesday, the team is rolling back to the prompt from before, which now lives somewhere in git history and is also annoying to recover.
With evals, the same prompt change runs against the regression set in CI. The break is caught before merge. The senior engineer keeps their Monday.
The Diagnostic Question
Before you decide whether your AI feature is ready for production, ask one question.
If we change the model tomorrow, do we have a way to tell whether our customers will notice in the wrong direction?
If yes, you have evals. If no, build the first one, the regression on the top 20 inputs, before you change anything else. The day model providers ship a new version, and they will, often, is not the day to discover your eval suite does not exist.
When to Add the Heavier Eval Infrastructure
Around the time you have multiple AI features in production, the lightweight script-based approach starts to feel small. You will want a dashboard that tracks eval scores over time. You will want failure cases automatically routed back into the test set. You will want eval-driven prompt rewriting and canary deploys for prompt changes.
Add those then. Not before. A seed-stage team running five well-chosen evals on a script in CI beats a seed-stage team operating a fancy eval platform with no test cases inside it. The infrastructure is downstream of the discipline.
Let’s Talk
If you are running an early-stage team shipping AI features and trying to figure out how to actually know whether your features work in production, that is the kind of question I work through with founders. I take on senior async architecture and product-engineering work for teams making exactly these calls. If that sounds like your situation, reach out.
