Measure the workflow before you automate it
A useful AI system starts with a clear baseline, real examples and an agreed definition of a good result.
By VPFUnited Solutions
In applied AI, model choice is only part of the problem. Teams often begin without defining the current level of speed, quality or effort, which makes the outcome difficult to evaluate later.
Start by observing the existing workflow honestly. Include rework, checks, exceptions and the time spent asking an experienced colleague for help. This baseline shows where time is really going and what an improvement would mean.
Build an evaluation set before polishing the prototype. Use representative examples with reviewed answers, keep them separate from development and rerun them whenever the system changes. This turns prompt and model decisions into evidence rather than preference.
Set the confidence threshold as a product and operational decision. Decide which cases may continue automatically, which need human review and what happens when the system is uncertain. Those choices determine both risk and cost.
Make each important output traceable to the information behind it. Clear sources help reviewers check a result, correct mistakes and understand when the system should not be trusted.