Ship AI features that survive users → Prompt to Production

1. The demo-to-production gap

You can build an impressive AI demo in an afternoon. You can build an AI feature that survives real users for months — and that's an entirely different job.

Most engineers don't see the gap until they've fallen into it. The demo runs cleanly on the inputs you tested. The production system gets the inputs you didn't test. Edge cases. Adversarial users. Inputs the model wasn't trained for. Latency spikes. Cost overruns. Outputs that drift over time as the underlying model gets updated. This is the gap where most AI features die.

The skill that closes the gap is its own discipline. It's not prompt engineering. It's the engineering around the prompt — the evals, the guardrails, the observability, the fallback paths, the cost controls.

2. What Prompt to Production covers

[[prompt-to-production]] is the deep dive on this. It walks you through the whole production stack for AI features: how to write evals that catch regressions before they ship, how to set up the guardrails that keep outputs in bounds, how to observe what the system is doing in production, how to handle the cases the model gets wrong.

The course is built around the engineering reality that AI features fail in ways traditional features don't. The failure modes are different. The instrumentation has to be different. The on-call rotation has to be different. You learn what the differences are and how to build for them.

3. Who should take it next

If you've built a prototype with the model and you're about to ship it to real users — or if you've shipped already and you're learning the hard way that production is different — [[prompt-to-production]] is the next stop.

It's the right course if you're a working engineer being asked to take an AI feature from "it works in dev" to "it works at scale, reliably, indefinitely."