AI features can look impressive in a prototype and become much harder once real users depend on them. Production systems have to handle incomplete data, unexpected inputs, changing models, latency, and cost.
The first step is defining exactly what the AI feature should accomplish. A focused workflow with a measurable outcome is usually more valuable than a general assistant trying to solve every problem.
Reliable AI products also need evaluation from the beginning. A representative set of real questions or tasks gives teams something measurable whenever a prompt, model, or retrieval strategy changes.
Production AI also needs graceful failure. Human escalation, fallback responses, monitoring, and sensible cost limits should be designed into the system rather than added after problems appear.