Every team can build the demo now. Wire a model to a prompt, feed it a happy-path input, and it dazzles in the meeting. The trouble starts the moment real users arrive with inputs no one scripted.
The demo optimises for the wrong thing
A demo is optimised to succeed once, on stage. A product is optimised to fail gracefully a million times, unattended. Those are almost opposite engineering goals, and confusing them is why so many AI initiatives stall after the pilot.
What production actually requires
Evaluation you trust. Guardrails on inputs and outputs. Cost you can predict at scale. Observability so you can see what the model actually did. And a fallback for the day the provider has an outage, because it will.
// A demo:
const answer = await model.generate(prompt);
// A product:
const answer = await withGuardrails(
withEval(
withFallback(() => model.generate(prompt))
)
);Build the boring parts first
We start AI engagements from the eval harness, not the prompt. If you can't measure whether a change made the system better, you're not engineering, you're gambling with extra steps.
The demo is the easy 20%. The other 80% is why you hire engineers.