Teams often assume adding AI means a rewrite. It rarely does. The fastest path is usually a thin, well-tested layer alongside the code you already trust, not a replacement for it.
The rewrite instinct usually comes from a good place: a new capability feels big, so it seems like it deserves a big architectural response. In practice, the products that ship AI features fastest and most reliably are the ones that treat the model call as one more service in the stack, not as a reason to start over.
Start at the seam
Find the one workflow where AI clearly helps, wrap it behind a single interface, and ship that. A narrow first integration teaches you more about your data, your users, and your failure modes than any broad plan ever will. The lessons from that first integration, what kinds of inputs break it, what latency users actually tolerate, what a good fallback looks like, apply directly to whatever comes next.
Picking the seam well matters. The best first integration is a workflow that is genuinely painful today, has a clear definition of a good outcome, and fails gracefully if the model gets it wrong. A workflow where a wrong answer is expensive or hard to detect is a bad place to start, no matter how impressive it would look.
- Isolate the model call behind one service boundary you can test and swap.
- Write evals before you tune prompts, so quality is measured, not felt.
- Add guardrails and a sensible fallback for when the model is unsure.
- Log every request and response so you can improve with real usage.
Evals before prompt tuning
It is tempting to start iterating on prompt wording the moment the integration works, tweaking phrasing until the output feels right. Without a fixed set of test cases, that process is optimizing against a moving target: each change might fix the example in front of you while quietly breaking three cases you tested an hour ago and have since forgotten.
Writing a small, real eval set before touching the prompt turns that guesswork into measurement. It does not need to be large or elaborate to be useful. A few dozen representative cases, pulled from real usage or realistic scenarios, is enough to tell you whether a prompt change is actually an improvement.
Ship the smallest useful integration first. Scope grows naturally once something real is in front of users.
Treat prompts like code
Prompts belong in version control, behind the same review and evaluation gates as the rest of your system. A prompt change is a behavior change, and it deserves a test that proves it did not regress the cases you already got right, the same way a code change deserves a passing test suite before it merges.
Teams that treat prompts as loose strings scattered through the codebase, edited freely without review, tend to see quality drift over time in ways nobody can quite explain. Teams that treat them as reviewed, tested artifacts catch that drift before it reaches users.
Design for the unsure case
The difference between a toy and a product is what happens when the model is uncertain. A production integration has an explicit path for low-confidence output: ask a clarifying question, fall back to a deterministic default, or route to a human, rather than presenting a guess as fact.
Building that path is not extra work bolted on after the core feature is done, it is part of what makes the feature trustworthy enough to ship. A feature that is right most of the time but fails silently the rest of the time will erode user trust faster than a feature that is visibly cautious about what it does not know.
None of this requires touching the rest of the application. It requires treating the new capability with the same rigor as everything else already in production, which is usually the whole difference between a feature that ships once and a feature that keeps working.
Latency and cost are product decisions, not afterthoughts
A model call is slower and more variable than a database lookup, and ignoring that until after launch tends to produce a feature that feels sluggish in exactly the moments users notice most. Deciding upfront whether a workflow can tolerate a few seconds of latency, needs streaming output to feel responsive, or should run asynchronously in the background changes the integration architecture, not just the polish.
Cost follows the same pattern. A feature that is cheap to run against a demo's handful of requests can look very different once real traffic hits it. Modeling expected volume and cost before launch, rather than discovering it from a bill, is part of designing the integration properly rather than an operations concern to deal with later.
The seam becomes the template
Once the first integration is live, stable, and evaluated, it stops being a one-off and becomes the pattern the rest of the team reaches for. The service boundary, the eval harness, the fallback logic, all of it generalizes to the next workflow with far less effort than the first one took. This is the real payoff of starting narrow: not just a working feature, but a proven, repeatable way of shipping the next ten.