Building Production AI Systems

Most AI demos die the moment they meet production. Here's what actually keeps an AI system alive.

Start with the failure modes, not the model#

Pick the cheapest model that can't embarass you, then invest in the scaffolding around it.

type Evaluator = (output: string, expected: string) => Score;
const strict: Evaluator = (o, e) => (o === e ? 1 : 0);

Heuristic

If you can't evaluate it, you can't improve it. Write the eval before the prompt.

Logs, traces, and a feedback loop turn a flaky demo into a system you can trust.

Part of the AI Engineering for Builders series. The full production picture — guardrails, model routing, caching, and the monitoring loop — is in Part 6 — Production AI Architecture. Start the series with Part 1 — The AI Engineering Mental Model.