Hey all, when building AI automation, I’m often bothered by reasoning models because that reasoning adds quite unpredictable delays and token consumption to your workflows. On the other hand, benchmarks consistently show significantly better scores with higher reasoning, so you can’t just ignore it. One thing I’ve started implementing in workflows where output quality is crucial is to add a reasoning key to the structured output of the AI call. I disable the model’s native reasoning so it starts outputting the first token immediately, but it still writes out a reasoning field for me. Then a second key, output or response for example, holds the actual result as a string or whatever fits the use case. My thinking is that this might be just as effective as native reasoning, with the added benefit of more control. It’s easier to cap it to a word limit, or make it more token-efficient by just asking it to use bullet points for example. So I wanted to get some confirmation on whether this is actually a valid substitute for native reasoning, and what the best practices are for getting the benefits of reasoning while keeping some predictability in execution speed and token usage. submitted by /u/duv_guillaume
Originally posted by u/duv_guillaume on r/ArtificialInteligence