It is common for AI tools to perform well in controlled demonstrations but face challenges in the variability of a real-world production environment.
The Challenge: Maintaining trust with operational teams.
If a new tool fails unexpectedly, operational staff may hesitate to rely on it, reverting to manual processes to ensure accuracy. This "Reversion Threshold" is a critical metric for adoption.
Recommendation: Constrained Output & Strict Schemas.
To build trust, we recommend limiting the "creativity" of the model in operational workflows. Use Constrained Decoding (forcing JSON schemas) and rigid validation logic. It is better for a system to return an error than a hallucination. Consistency breeds trust.