I started separating AI research into facts, assumptions, and questions

I've been experimenting with AI-assisted startup research, and I noticed a small problem with the way I was reading the output.

AI tends to put these three things surprisingly close together:

Facts

Things that can actually be verified.

Assumptions

Things that might be reasonable, but haven't been proven yet.

Questions

Things where there simply isn't enough information to make a good decision.

For example, imagine AI says:

"Small agencies are likely to pay $299/month for this product."

That's not really a fact.

It's an assumption.

And it should be treated differently from:

"This competitor currently charges $299/month."

That is something you can actually check.

So I've been testing a simple habit:

Whenever AI produces startup research, I try to classify important claims as:

FACT → ASSUMPTION → QUESTION

Then I ask:

What would I need to verify before making a decision?

It changes the usefulness of the output quite a bit.

Instead of ending with a polished research document, you end up with something closer to an investigation checklist.

I think this matters more as AI gets better at producing convincing research.

The harder problem may not be getting information anymore.

It may be knowing which parts of the information deserve confidence.

I'm curious how other founders handle this:

Do you explicitly separate AI output into verified information and assumptions, or do you mostly review the source material afterward?