From Prototype to Production

A demo optimizes for the happy path; a product survives the long tail. Most prototypes break not on dramatic hallucinations but on small workflow recovery failures — a user replies 'good' instead of confirming, changes information mid-flow, or asks a side question. The gap between impressive and dependable is where real work begins.

Production AI is a workflow layer, not a single assistant. One interface quietly supports many kinds of work — questions, onboarding, transactions, analysis, escalation. Routing becomes the first product decision: before answering, the system must know what kind of work it's doing, then match the path, model, and cost to the task.

Evals are the regression system for AI products. Did routing improve? Did retrieval ground the answer? Did latency get worse? Did the workflow complete? Evals turn subjective 'it feels better' into a release gate, so every change is measured against the failures you've already seen.

In high-stakes domains, a confidently wrong answer is the most expensive failure. Confidence thresholds, grounded context, and explicit abstention let the system decline rather than guess. Caching becomes policy: fast when safe, careful when stakes are high, never stale where it matters.

Human judgment compounds only when the loop closes. An expert correction that lives in Slack repeats the same mistake tomorrow; a correction that becomes context, a workflow rule, or an eval case makes the system better. The art is knowing when to involve a human — and how to capture what they teach.

You can't improve what you can't trace. Observability is the full path of every request: which route was taken, what context was used, what the model saw, whether the cache hit, where the workflow was, whether a human reviewed, and whether the eval passed. The trace is the audit trail that makes the system trustworthy.

Scale is an accountability shift. The question is no longer whether the system can produce one good answer, but whether it can route correctly, preserve state, assemble the right context, cache safely, involve humans when needed, detect regressions, and improve after failure. The model answers; the system improves.