Most ai api integration best practices lists are really general API advice with the word AI added. Retries, timeouts and authentication matter, and they are not what makes these integrations different.
What makes them different is that the response is non-deterministic, priced per token, and capable of returning something well-formed and wrong. Everything below follows from those three.
Treat The Output As Untrusted Input
This is the single most important habit. A model returns text that looks like what you asked for. It has not been checked against anything.
Validate the shape before using it. If you asked for JSON, parse it and validate against a schema rather than assuming. If you asked for one of five categories, check it is one of the five. If you asked for a number, confirm it is a number and inside a plausible range.
Use structured output modes where the provider offers them. They reduce malformed responses substantially without removing the need to validate, because a syntactically valid response can still be semantically wrong.
Cap Spend Before You Need To
Token billing means a bug is a bill. A retry loop around a large prompt can produce a genuinely surprising invoice overnight.
- Set a hard limit on tokens per request and per run, and fail loudly when it is hit rather than truncating quietly.
- Cap the number of retries. Three is usually plenty; unlimited retries against a persistent failure is just spending.
- Track cost per operation, not only monthly totals. A monthly figure hides one run that cost forty times the median.
- Alert on outliers. The useful signal is a run that behaves unlike the others, not the aggregate.
Handle Failure Modes That Are Specific To This
Ordinary API failures are familiar: timeouts, rate limits, five hundreds. Use exponential backoff and move on.
The ones worth designing for are different. Truncated responses when the output limit is reached mid-structure, which produces JSON that fails to parse. Refusals, where the model declines and returns prose instead of your expected format. Latency variance, where the same call takes two seconds or forty depending on load.
That last one matters more than people expect. If your integration sits in a request path a user is waiting on, design for the slow case: a queue and a callback, or a streamed response, rather than a synchronous call and a hopeful timeout.
The failure that costs the most is not an error. It is a plausible answer that was wrong, accepted because nothing checked it.
Hannah Berg, Lead AI Engineer, Engineered With AI
Keep The Prompt Out Of The Code
Prompts change far more often than the code around them. Embedding them as string literals means every wording change is a deployment, and comparing versions becomes archaeology.
Keep them as versioned templates with the variables clearly separated. Log which version produced which output. When quality shifts, you want to be able to answer what changed, and "somebody edited the prompt three weeks ago" is only useful if it is recorded.
Log Enough To Reconstruct A Decision
For every call, store the full input including the prompt version, the full response, the model and parameters, the token counts and the latency.
This looks excessive until the first time somebody asks why the system did something odd last Tuesday. Without it you are guessing, and with a non-deterministic component you cannot simply re-run and expect the same answer.
Mind what you are storing. If prompts contain personal data, those logs are now a data protection consideration, with a retention policy and access controls to match.
Assume The Provider Will Change
Models get deprecated, prices change, behaviour shifts between versions. Pin to a specific model version rather than a floating alias, so an upgrade is a decision you make rather than one that happens to you overnight.
Keep provider-specific code behind a thin interface of your own. Not an elaborate abstraction layer, just enough that swapping provider means changing one module rather than auditing the codebase.
And keep a small evaluation set, twenty or thirty real inputs with known-good outputs, running on a schedule. It is the only way to notice that quality drifted before a customer does.
The Short Version
Validate every response. Cap tokens, retries and spend, and fail loudly. Design for the slow path. Version prompts outside the code. Log enough to reconstruct any decision. Pin the model and keep the provider swappable. Run an evaluation set on a schedule.
None of it is exotic. It is the discipline you would apply to any component that spends money and can be confidently wrong, which is exactly what this is.
Integrating a model into something real?
Tell us where the call sits in your system and what happens if it returns something plausible but wrong. We will tell you which of these controls you actually need first.