Large language models are remarkable, but a model call is not a system. The gap between "this prompt works in the playground" and "this runs unattended against real customer data every day" is where most AI projects quietly fail. This post walks through how we close that gap.
Treat the model as one component, not the whole system
A production AI pipeline is mostly ordinary software engineering with one probabilistic step in the middle. The model handles the reasoning, extraction, or generation — everything around it is your responsibility:
- Input validation before the call, so garbage never reaches the model.
- Structured output out of the call, so the next step can trust the shape.
- Retries and fallbacks around the call, because networks and models both fail.
The model is the easy part. The reliability lives in everything you wrap around it.
Force structure on the output
Free-form text is convenient for humans and miserable for software. We always constrain the model to a schema and validate the result before trusting it:
const ResultSchema = z.object({
category: z.enum(["billing", "technical", "sales"]),
urgency: z.number().min(1).max(5),
summary: z.string().max(280),
});
const parsed = ResultSchema.safeParse(JSON.parse(response));
if (!parsed.success) {
// Re-prompt, fall back, or route to a human — never silently continue.
}
If the output doesn't parse, that's a signal — re-prompt with the error, fall back to a simpler path, or escalate to a person. What you never do is pass an unvalidated blob downstream.
Evaluate before you ship
Prompts are code, and untested code is a liability. Before anything goes live we build a small evaluation set of real, representative inputs with known-good outputs, then measure accuracy on every prompt change. It doesn't need to be fancy — a spreadsheet of fifty examples catches the regressions that "it looked fine when I tried it" never will.
Keep a human in the loop where it counts
Full autonomy is the goal for low-stakes, high-volume tasks. For anything irreversible or customer-facing, design the approval step in from the start. The best pipelines make the human's job reviewing instead of doing — a ten-second confirmation rather than a ten-minute task.
This is the approach we use on every AI engagement at Nico Technologies. If you've got a complex, high-value process that feels ripe for automation, let's talk — we'll tell you honestly whether AI is the right tool for it.