Writing Evals for a Feature Nobody Has Used Yet
A working note on writing llm evals before launch — what matters, what does not, and where projects usually go sideways.
There is a short version of writing llm evals before launch and a long one. The short version fits on an index card, and most projects would be fine if they stopped there.
Where writing llm evals before launch usually goes wrong
The engineering part is rarely the blocker. The blocker is that nobody wrote the goal in one sentence, so every meeting reopens the same argument.
Write the outcome. Write the number that proves it.
If a new hire could not repeat the goal back to you, the scope is still too loose to estimate.
What this looks like in practice
In projects like these the shape repeats. Someone maps the current process, finds three painful steps, and discovers only one of them justifies real engineering.
A team we would typically advise starts with the step generating the most back-and-forth email. Not the most interesting one.
The first version covers the common case and a human handles the rest. That is the design, not a compromise.
Common mistakes
The expensive one is scoping to the edge case. A requirement that affects two percent of users can double the build.
The quiet one is skipping instrumentation, then guessing at causes for a month.
And the recurring one is buying flexibility nobody uses. Every configuration option is a support burden with a delayed invoice.
The engineering view
From inside the codebase, writing llm evals before launch comes down to three questions. What happens when a step fails halfway. Who gets paged. And how you undo it.
Design for partial failure early. The third step will fail after the first two succeeded, eventually.
Add retries with jitter and a ceiling before you need them. Retry storms are self-inflicted outages.
A sequence that tends to work
- Write the outcome and the metric, one sentence each, agreed by whoever signs off.
- Map the process end to end, including the manual steps people are slightly embarrassed about.
- Pick the single highest-friction step and ignore the rest for now.
- Ship a narrow version behind a flag to a handful of real users.
- Watch it for two weeks against the number from step one.
- Expand only where the data says it pays.
Step three is where teams cheat. Keeping it honest turns a six-month project into a six-week one.
Practical guardrails
- Instrument before you optimise. Guessing at bottlenecks costs more than measuring them.
- Keep a rollback path for anything touching customer data.
- Document the decision, not just the result.
- Set a review date ninety days out.
- Cap spend and volume in code, not on the invoice.
The honest trade-offs
Going fast now usually means paying interest later. That is fine if you know the rate and have a date to refinance.
Going slow now to avoid rework only pays off if the requirements hold. Early on, they rarely do.
Things people believe that are not quite true
That more tooling reduces risk. Usually it moves the risk somewhere less visible.
That a rewrite resets the clock. It resets the bugs too, and you get a new set.
That the team will document it afterwards. They will not, unless it is part of the definition of done.
Frequently asked questions
How long does writing llm evals before launch usually take?
A narrow first version is normally four to six weeks. Anything quoted at three months with no shippable slice in between is a risk, not a plan.
What is the most common mistake with writing llm evals before launch?
Scoping too wide. Covering every case in version one delays feedback and inflates cost with no matching benefit.
Do we need a dedicated team for this?
Not at the start. One owner with a few hours a week plus a small build team is enough until the first version proves value.
How do we know whether it worked?
Pick the number before you build: hours saved, error rate, response time or conversion. Compare a two-week window before and after.
What should we do first?
Write one sentence describing the outcome of writing llm evals before launch, then map the workflow it touches. Both take an afternoon and remove most of the guesswork.
Conclusion
The useful move on writing llm evals before launch is almost always the smaller one. Ship a narrow slice a real user can touch this month, measure it, then decide what deserves the next four weeks.
Everything gets easier once something is live.
Related reading and next steps
- AI product engineering — how we run this kind of work.
- All Augere Labs services.
- More writing from the team.
Want a second opinion on writing llm evals before launch for your setup? Book a 30-minute call. We will say plainly if it is not worth building.
FAQ
Frequently asked questions
How long does writing llm evals before launch usually take?+
A narrow first version is normally four to six weeks. Anything quoted at three months with no shippable slice in between is a risk, not a plan.
What is the most common mistake with writing llm evals before launch?+
Scoping too wide. Covering every case in version one delays feedback and inflates cost with no matching benefit.
Do we need a dedicated team for this?+
Not at the start. One owner with a few hours a week plus a small build team is enough until the first version proves value.
How do we know whether it worked?+
Pick the number before you build: hours saved, error rate, response time or conversion. Compare a two-week window before and after.
What should we do first?+
Write one sentence describing the outcome of writing llm evals before launch, then map the workflow it touches. Both take an afternoon and remove most of the guesswork.
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