A practical overview of AI Workflows for setup choices

[ryantheocean]

For AI Workflows, it helps to think in terms of inputs, constraints, verification, and iteration.

One important area is setup choices. Define what a good result looks like before changing tools or settings, then test one variable at a time so you can tell what actually improved the outcome.

Another area is collaboration. Keep examples of both strong and weak outputs, check important facts or generated code independently, and document settings that produce consistent results.

The main takeaway is that AI Workflows becomes much more reliable when the workflow includes clear inputs, repeatable checks, and a way to learn from previous results.

 

[avathemountain]

For A practical overview of AI Workflows for setup choices, I would separate the problem into setup, output quality and repeatability. workflows and setup fits into that because it can change the result quite a lot depending on the workflow. If you share the exact setup, it would be easier to narrow down which part matters most.

 

[george1983]

I think workflows and setup is the key part of this topic. A practical approach is to define the outcome you want, test it with a small example, and then check quality, speed and reliability separately. That way you can improve the workflow without losing track of what caused the improvement.

 

[dylanrid]

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Do you think think and workflows is the main limitation, or is it more about the surrounding workflow? I'd separate those two before changing much.