AI Workflows discussion: mistakes and lessons

[nathan4833]

I wanted to start a broader mistakes and lessons thread around AI Workflows.

A useful angle is deployment, but it also connects with setup choices. Different tools and workflows can produce very different results even when the goal is similar.

It would be interesting to compare practical examples, experiments, setups, or lessons that show where AI Workflows is genuinely useful and where it still needs improvement.



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[mattonworld]

The point about workflows and lessons is interesting. In practice I would keep the first test simple, record what worked, and only add extra complexity if it solves a specific limitation. That should make it easier to tell whether the change is actually helping.

 

[hannahsout]

One thing worth trying with AI Workflows discussion: mistakes and lessons is a controlled comparison around workflows and lessons. Use the same input for each test and note what changes in the output rather than judging from a single run. That way you can improve the workflow without losing track of what caused the improvement.