Noah Luttinger (NLW)

Or, for our purposes, you can run an actual process around it, where the first output is just that, the first pass that then gets built upon.

This is not dissimilar from the cliche burndown I just mentioned, but is more broad.

In a single prompt, you could, for example, say, draft a first version of a particular artifact, maybe an essay, maybe a pitch deck, maybe a presentation.

Then red team it and list the top five ways it's generic.

Rewrite a V2 that fixes each issue, and then explain why you changed what you changed.

This sort of self-critique is incredibly valuable, and you can even add an additional dimension where you give it a context lens through which to critique itself.

So for example, instead of just generically saying, list the top five ways it's generic, you could say, list the top five ways it feels too generic for an undergraduate audience.

further, one additional element that you can add to that sort of self-critique is to have different models do the critiquing as well.

Now, not everyone is an insane person like me with premium subscriptions to every single LLM, but even for those of you who are, for example, just using open AI models, there is a big breadth of different approaches that these different models represent.

Something I did yesterday is I was architecting a whole new pitch for a part of the super intelligent business, which is coming in 2026.

I had been working through the architecture of the pitch with GPT-5 thinking, which was a combination of inline research, strategic thinking, and messaging discussion, and it was doing a pretty good job.

But if you've used GPT-5 thinking and O3, they feel fundamentally like very different models.

Not better or worse, just very different.

O3 is much more clinical.

It's much more likely to give you lists and charts and tables.

There's a certain concision and precision of thought that O3 goes for that GPT-5 thinking doesn't have in the same way.

Which is not to say that O3 is better for all use cases.

O3 was very hard to get certain types of writing out of because of how badly it wanted to apply that sort of concision and chart-based information presentation.

But what I did as part of this process was that at some point, fairly deep into the conversation, I mean, after I had been talking back and forth with it across about 15 different outputs in a single thread, I turned that whole thread into a link and shared it and flipped over to a new chat in the same app, toggled that new chat to the O3 model instead of the five thinking model,

asked it to review and basically make a set of critiques and changes and argue for what it thought we, which was me and GPT-5 thinking together, were missing as part of the whole conversation.