A compelling demo can be built from a handful of carefully selected prompts. Production brings repetitive, incomplete and high-risk inputs. The team then discovers that nobody agreed on what a good answer is, when a person must review it or how much one completed task may cost.
Define the completed user task
Replace broad features such as document summarization with a measurable job, for example reducing classification time or identifying missing contract clauses. Capture the current time and error pattern before adding AI.
Build evaluation from real input distributions
Include frequent, important and costly-to-miss cases. For open-ended work, state required facts, forbidden claims and citation expectations so models and prompts can be compared consistently.
Design the failure path
Low-confidence results can request more information, route to a specialist or require review before external delivery. A generic error screen does not complete the workflow.
Release to a narrow group first
Store retrieval quality, generation quality, latency, tokens, routing and user outcome per request. A small rollout reveals workload and review behavior without making the entire operation depend on an unproven path.