How do you improve evaluation when working with AI Workflows?

[skykai]

I am trying to improve my approach to AI Workflows and could use some practical advice.

I am particularly interested in evaluation and collaboration. I would like to compare approaches that people have actually used rather than just theoretical suggestions.

Which approach would you recommend and why?

 

[meganwol]

The point about improve and evaluation 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. The original point — "I am trying to improve my approach to AI Workflows and could use some practical advice. I am particularly interested in evaluation and collaboration. I would like to compare appro…" — gives a useful starting point for that comparison.

 

[riverella]

The point about improve and evaluation 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. If you share the exact setup, it would be easier to narrow down which part matters most.

 

[reece_north]

One thing worth trying with How do you improve evaluation when working with AI Workflows? is a controlled comparison around improve and evaluation. Use the same input for each test and note what changes in the output rather than judging from a single run. It is usually easier to troubleshoot when each change is tested on its own.

 

[ivythesignal]

For How do you improve evaluation when working with AI Workflows?, I would separate the problem into setup, output quality and repeatability. improve and evaluation fits into that because it can change the result quite a lot depending on the workflow. It also gives you a cleaner baseline if you want to compare tools or settings later.