A compelling prototype can open a conversation. A reliable product needs a team that can handle everything around the model.
Pick a workflow, not a technology
Start with a bounded task and a person who owns its outcome. Decide what a useful result looks like, what must never happen, and where a human will review the output. That makes model selection a practical decision.
Build an evaluation set early
Collect representative examples with permission, remove unnecessary personal information, and define how outputs will be judged. Include ambiguous requests and failure cases. Re-run those evaluations when you change a model, prompt, or data source.
Design the supporting system
Production AI needs reliable data access, identity controls, monitoring, cost limits, and a clear failure path. Retrieval quality, latency, and user experience can matter as much as model capability. Bring software, data, and platform expertise into the conversation early.
Ship a smaller, observable version
Start with a defined group of users and a workflow you can measure. Record useful signals without collecting unnecessary sensitive content. Use what you learn to decide whether to expand, revise, or stop. Progress means a better working system, not simply a bigger demo.