Building a custom GPT for a ministry training program gets treated as a finished deliverable the moment the training session ends and everyone walks out satisfied. What actually happens afterward, watching what the tool needs six months into real use versus what it needed on launch day, turned out to be a completely different set of problems than the ones solved during initial development.
The Assumption That Breaks First
Deployment day testing happens against the world as it exists on deployment day. The knowledge base reflects current procedures, current terminology, current organizational structure. The system prompt gets tuned against that snapshot, and by every reasonable measure at that moment, it works well.
The quiet assumption underneath that success is that the ministry's procedures, terminology, and structure will hold still. They do not. Procedures get revised. Terminology shifts, sometimes subtly, in ways that would not even register as a change to someone inside the institution but that matter enormously to a retrieval system trained against the old phrasing. Organizational responsibilities move between departments. None of this is dramatic or sudden. It accumulates quietly, and a tool that was accurate on day one can become gradually less accurate without any single obvious moment where it broke.
