Test the workflow your team would actually use.
A good-looking AI result is worth opening, using, and trying to break.
When I look at an AI-generated browser tool, I want to know whether the controls work. I also want to know what happens when somebody gives it a blank field, opens it on a phone, or uses a keyboard instead of a mouse.
Those questions belong in the evaluation. A screenshot cannot answer them.
My own model-comparison work includes generated HTML examples, browser checks, delivery failures, and assisted repairs. The useful lesson for anyone buying or running a workflow is to define the work you need back and evaluate the whole attempt.
Start with a task you can judge
Choose a small piece of real work that can be represented with approved or fictional data. For a reporting workflow, that might be a packet with known totals and deliberate missing fields. For a small app, it might be a form with a few clear rules and a result you can calculate yourself.
Write down the expected behavior before comparing tools. Otherwise, it is easy to let an attractive output change your definition of success halfway through the test.
Keep the failures in the record
If one attempt produces excellent work and another produces nothing usable, both outcomes matter. Reviewing only the successful files gives you an incomplete picture of what a team can depend on.
Record whether a file arrived, whether it opened, whether it did the job, and whether someone had to repair it. Keep the time spent waiting and the time spent helping separate when you can.
A repaired result may still be valuable. It just answers a different question from an unassisted result. The person buying or operating the workflow needs to know how much help was required.
Check the surrounding system
A failure may come from the model, the instructions, an integration, or the way the test was run. If a response was cut off by an output limit or a connection timed out, record that instead of silently treating every failure as the same kind of model error.
The distinction helps decide what to change. Better instructions will not fix every transport problem. A different model will not automatically fix an unclear acceptance test.
Make the result useful to the team
A small evaluation record can include the task, the approved inputs, the tool and settings used, expected behavior, observed result, and any repair. Add the failure cases that would matter in normal use.
For a browser tool, I would include keyboard use, a smaller viewport, invalid inputs, and the action the user takes next. For a workbook, I would inspect formulas, exclusions, and how the output relates to the source records.
Bring one workflow into the conversation with an example of a good result. That gives your technical team and the people doing the work something concrete to judge together.
This is an evaluation method drawn from my own project work. It is not a universal model ranking, a controlled academic study, or a promise that a tool will behave the same way on your task. The full record is in the field notes.