There is a wide gap between an AI API call that works in a notebook and one that works in production for a year. The notebook version assumes the network is perfect, the output is well-formed, the cost is trivial, and the user always sends reasonable input. Production assumes none of those things. The practices below are the ones that closed that gap for us, written as opinions rather than hedged suggestions, because the hedged version is useless to anyone trying to ship.
An AI API is a hosted model behind an HTTP endpoint. You send a prompt and parameters, you get a generated response and a token bill. Everything good about building on one comes from respecting two facts about that endpoint: it is non-deterministic, and it is metered. Most best practices are just disciplined responses to those two facts.
These are not generic platitudes about "iterating on prompts." They are specific things to do, with the reasoning attached, so you can decide which ones apply to your situation and which you can skip.
Design the Prompt Like an Interface, Not a Wish
The instinct of new teams is to write the prompt the way you would ask a smart colleague for a favor. That works until you need consistency. A prompt is the interface contract between your code and the model, and contracts should be explicit.
Separate the instructions from the data
Put your stable instructions in a system prompt and the variable user content in a separate, clearly delimited block. Mixing them invites prompt injection and makes the model confuse instructions with content. A clear boundary like delimiters or XML-style tags measurably improves how reliably the model follows your rules.
Specify the output shape before you need it
If you will parse the response, tell the model the exact structure and request structured output mode where the provider supports it. Asking for "a list" gets you prose half the time. Asking for a JSON object with named fields, validated against a schema, gets you something your code can trust.
Treat Cost as a First-Class Constraint
Token cost is not an afterthought you optimize later. It is a design input that shapes architecture. A feature designed without a token budget will eventually be redesigned with one, usually after an uncomfortable invoice.
Choose the smallest model that passes your evaluation
Teams reflexively reach for the largest, most capable model. Often a smaller, cheaper, faster model passes the same evaluation set at a fraction of the cost. Start small and escalate only when the numbers force you to. The trade-offs between approaches lay out exactly when paying for the bigger model is worth it.
Cache what does not change
If many requests share the same long system prompt or the same reference documents, use prompt caching where the provider offers it, or cache full responses for identical inputs. Repeated identical work is the easiest money you will ever save.
Build for Failure From the First Commit
The endpoint will fail. It will rate-limit you, time out, and occasionally return garbage. Build as if that is normal, because it is.
Wrap every call in retries with backoff
Use exponential backoff with jitter, distinguish retryable errors from terminal ones, and cap total retry time so a stuck request does not block a user forever. This single habit prevents the most common "the AI is down" incidents, most of which are not outages at all. Our list of costly first-integration mistakes shows how often missing retries is the real culprit.
Always have a fallback path
When the model fails or returns something unusable, decide in advance what the user sees. A graceful "we could not generate that, try again" beats a stack trace every time, and a deterministic fallback beats both where one exists.
Validate Output Before You Trust It
The model is a creative collaborator, not an oracle. Treat its output the way you would treat input from an untrusted source, because functionally that is what it is.
Parse defensively and validate against a schema
Never assume the response is valid JSON or contains the field you asked for. Parse inside a try-catch, validate against a schema, and handle the failure explicitly. Plan for the one-in-a-hundred malformed response, because at scale it happens constantly.
Keep a human in the loop where stakes are high
For any output that triggers a financial, legal, or irreversible action, require human confirmation. Autonomy is appropriate for low-stakes, easily reversible tasks and reckless for the rest.
Measure Quality Continuously
You cannot improve what you do not measure, and prompt quality drifts silently as you edit.
Maintain an evaluation set
Keep a small, representative set of inputs with the qualities you expect in the output, and run it on every prompt or model change. This turns "I think this is better" into "this passes 47 of 50 cases, up from 44." The metrics that actually matter detail which signals to track and how to interpret them.
Log enough to debug later
Store the prompt, the model, the parameters, the token counts, and the response for every call, with appropriate privacy controls. When something goes wrong in a week, this log is the difference between a five-minute fix and a guessing game.
Make Latency a Design Decision
Quality and cost get most of the attention, but latency quietly determines whether people actually use what you built. A response that takes eight seconds to appear feels broken even when it is correct, and users abandon the feature long before they judge its quality.
Stream when the user is waiting
If a human is watching the response appear, stream tokens as they generate rather than waiting for the full output. The perceived speed difference is enormous: text that starts flowing in half a second feels instant even if the full answer takes the same total time. Reserve non-streaming for background jobs where nobody is watching the cursor.
Trim the prompt for speed, not just cost
Long prompts cost more and generate more slowly, because the model must process every input token before it produces the first output token. Trimming context to what is relevant pays off twice, once on the bill and once on the clock. When latency is critical, this is often the highest-leverage change you can make.
Set latency budgets per surface
A background summarization job can tolerate ten seconds; a chat interface cannot. Decide the acceptable latency for each surface up front and treat a breach as a defect, not a fact of life. This framing pushes you toward streaming, prompt trimming, and smaller models before users complain.
Version Everything That Shapes Output
The output of an AI API is a function of the prompt, the model, the parameters, and the context. Change any of them and the behavior changes, which means all of them deserve the same version discipline you give code.
Treat prompts as code
Keep prompts in version control, review changes to them, and tie each change to an evaluation run. A prompt edited directly in a config panel with no history is a liability: when quality shifts, you cannot tell what changed or roll it back. Prompts are the most behavior-defining lines in an AI feature and deserve the most scrutiny.
Pin the model version explicitly
Providers update models, and a silent upgrade can change your output overnight. Where the provider allows it, pin to a specific model version and upgrade deliberately, re-running your evaluation set before you do. Surprise improvements are still surprises, and surprises in production are rarely welcome.
Frequently Asked Questions
What is an AI API and how is it different from a regular API?
An AI API is an HTTP endpoint that returns the output of a machine learning model rather than a fixed database record. The key difference is that responses are non-deterministic and priced by token volume, which means the same input can produce different output and the cost varies with the length of the text involved.
Should I use the biggest model available?
Not by default. Start with a smaller, cheaper, faster model and only upgrade when your evaluation set shows the smaller one cannot meet your quality bar. Many production features run perfectly well on mid-tier models, and the cost difference compounds at scale.
How do I keep prompt injection from breaking my app?
Separate trusted instructions from untrusted user content using clear delimiters, never let user input silently override your system prompt, and validate the output before acting on it. Treat any text that originates from a user as potentially adversarial.
Is prompt caching worth setting up?
If you reuse the same long system prompt or reference documents across many calls, yes. Caching repeated context can substantially cut both cost and latency, and the setup effort is modest relative to the savings on high-traffic features.
How much logging is too much?
Log enough to reconstruct any single request: the prompt, model, parameters, tokens, and response, subject to privacy and retention rules. The goal is to debug production issues without re-running them, while never storing sensitive user data longer than you must.
Key Takeaways
- Treat the prompt as an explicit interface contract, separating instructions from data and specifying output shape up front.
- Make token cost a design constraint, choosing the smallest model that passes evaluation and caching repeated work.
- Assume the endpoint will fail and build retries, timeouts, and fallbacks before you ship.
- Validate every response against a schema and keep humans in the loop for high-stakes actions.
- Maintain an evaluation set and detailed logs so quality is measured, not assumed.