AI Debate Guides

Where most AI debates go wrong

Setting a debate up takes thirty seconds. Getting one worth reading is a prompting problem, and three failure modes account for nearly every disappointing transcript.

The question has an obvious answer. “Should companies protect user data?” is not a debate — both models agree, then compete at agreeing more thoughtfully. A debatable question has informed people on both sides.

There are no constraints. “Is Python better than Rust?” produces a tour of considerations you could have written yourself. Add the team size, the deadline and the existing skills, and the models have to commit to something specific.

Nobody interrupts. Left alone, models converge on hedged agreement around turn six or seven because agreeableness is what they are tuned for. Typing “you both skipped X — address it” is the single highest-value action available, and it roughly doubles how long a debate stays useful.

How to read a transcript

A debate produces a lot of text and most of it is not the point. Three things are worth extracting: what both models asserted without either disputing it; the first genuine divergence and the assumption it rests on; and any specific claim that got raised and quietly never answered. That last one is often what was hardest to address.

What you will not get is a verdict. Nothing declares a winner, and any tool promising one is overselling. The output is a map of the disagreement, and reading it is your job.