AI engineering is the work of turning model behavior into a product system that can be tested, deployed, monitored, improved, and trusted within clear limits.

The lifecycle covers much more than choosing a model or writing a prompt. It includes problem framing, data work, evaluation design, system integration, release engineering, observability, incident response, and continuous improvement. Each stage produces decisions and files that the next stage depends on.

This chapter brings the production deployment section together and shows how an AI system moves from idea to production.