Understanding AI APIs: advanced workflows and best workflow

[isaacthesignal]

A useful way to understand AI APIs is to separate the goal, the input, the model or tool, and the way the output is evaluated.

One important area is advanced workflows. 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 best workflow. 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 APIs becomes much more reliable when the workflow includes clear inputs, repeatable checks, and a way to learn from previous results.

 

[connorlegend]

I think apis and workflow 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. It is usually easier to troubleshoot when each change is tested on its own.