Demos impress boards. Production impresses operators. The gap between them is evaluation — systematic, versioned, and tied to business risk rather than model vanity metrics.
Treat models as deployable artifacts
Every model version needs a changelog, a rollback path, and a harness that runs before promotion. We mirror CI/CD semantics: unit tests for deterministic components, integration tests for retrieval pipelines, and red-team suites for safety boundaries.
- Define failure budgets for hallucination and latency regressions.
- Log prompts and outputs with retention policies stakeholders can audit.
- Separate experimentation sandboxes from production inference planes.
When evaluation is first-class, AI stops being a special case in architecture reviews — it becomes software with uncertainty bounds, which is exactly what institutions need to approve.