A demo that works once is not a product. The hard part of AI engineering is everything after the first impressive output: making it reliable, measurable, and safe to put in front of real users.
Measure before you trust
The single biggest predictor of whether an AI feature survives production is whether the team built an evaluation harness early. Without evals, every prompt change is a guess and every regression is invisible until a user finds it.
Keep humans in the loop where it counts
The most successful AI products we ship don't replace people — they draft, suggest, and explain, leaving the final call to a human where the stakes are high. That single design choice removes most of the risk that stops AI projects from launching.
Ship the boring parts
Observability, logging, fallbacks, and EU-compliant hosting aren't glamorous, but they're what turn a clever prototype into something a business can rely on.