How We Evaluate a New Model Before Swapping It In
A working note on evaluating a new llm for production — what matters, what does not, and where projects usually go sideways.
There is a short version of evaluating a new llm for production and a long one. The short version fits on an index card, and most projects would be fine if they stopped there.
What people are actually asking
When someone raises evaluating a new llm for production, they normally mean one of three things: is this going to be expensive, is this going to break, or did we already make a mistake.
Worth separating those before the technical discussion starts. They have different answers.
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.
Mistakes teams make with evaluating a new llm for production
- Treating launch as the finish line. Most of the cost arrives afterwards.
- No named owner. Unowned work drifts, then the technology takes the blame.
- Designing for the rare case. Build the common path first.
- Skipping measurement. If nobody can tell whether it worked, you will keep paying regardless.
- Picking the tool first. That is the last decision, not the first.
How we approach it technically
Start with the data model. Most bad decisions here are downstream of a schema that made an assumption nobody revisited.
Then the failure modes. Then the interface. Interfaces are cheap to change; schemas and contracts are not.
Alert on rate of change rather than fixed thresholds. Quiet degradation is the failure that costs customers without waking anyone.
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.
What we insist on
One owner. One metric. One rollback plan. Those three cover most of the risk on work like this.
We also write the decision down with the date and the reasoning, because in six weeks somebody will ask why, and "it felt right" is not an answer that survives a board meeting.
Trade-offs worth saying out loud
Speed against flexibility. Cost against control. Managed services against ownership. None of these are free, and pretending otherwise is how a project goes over budget in month three.
Defaults are underrated. So is deleting a requirement.
Common misconceptions
“We need the best available option.” You need the option your team can operate at 2am. Those are rarely the same.
“We will fix it properly later.” Sometimes true. Write down what later means or it never arrives.
“This is a one-off.” Anything a customer touches becomes a product, with support attached.
Frequently asked questions
How long does evaluating a new llm for production 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 evaluating a new llm for production?
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 evaluating a new llm for production, then map the workflow it touches. Both take an afternoon and remove most of the guesswork.
Conclusion
The useful move on evaluating a new llm for production 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 evaluating a new llm for production 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 evaluating a new llm for production 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 evaluating a new llm for production?+
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 evaluating a new llm for production, then map the workflow it touches. Both take an afternoon and remove most of the guesswork.
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