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If your AI “won’t disagree,” the fix may not be a more forceful prompt. Ahmed El Otmani says his early attempts at generating short debates between brands produced polite exchanges that quickly converged. His most important design change was to give each debater a separate context and manage the turns outside the individual model responses. That is a useful design account, not a controlled demonstration that the approach works for every model or task.

Why did the debate bot keep agreeing?

El Otmani describes building DebaterX, a web tool that generates short debate videos between brands or mascots. In his early generations, the speakers sounded agreeable and drifted toward consensus instead of making a convincing case against each other. He treated that as a product-design problem: the system needed to preserve distinct positions across the exchange.

He attributes models’ tendency to seek agreement in part to assistant training and reinforcement learning from human feedback (RLHF). That is his explanation, not a causal finding established by the account: it reports no systematic model comparison, measurements, or cited study. The same caution applies to his observations that prompt-only approaches, aggressive instructions, and higher temperature did not solve the problem in his experiments. He does not specify a reproducible test protocol, model versions, or sample size.

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What changes did El Otmani report making?

Separate each debater’s context

Rather than asking one model response to sustain both sides in a shared conversational setup, he gave each debater its own context, persona, goals, and assumptions. The design aim is to keep one speaker’s position from blurring into the other’s. Each side should have a reason to argue from its own perspective, not merely react to the general conversation.

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Pass the rival’s argument into the next turn

He also describes managing turns externally: one side speaks, then the other receives the first speaker’s transcript framed as a rival’s mistaken statement to answer. This gives the second speaker a concrete target and lets the application control whose turn it is, rather than relying on an open-ended exchange to maintain the debate structure.

Write distinct character sheets

Separate contexts are only useful if the characters do not sound interchangeable. El Otmani says he used detailed character sheets to distinguish tone, rhetorical style, taboo moves, and recurring verbal habits. Those dimensions can shape how a character argues, not just what position it takes.

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Shape conversational pacing

He reports adding interruptions, trailing off, repetition, and incomplete sentences to make the exchange feel less like two uninterrupted blocks of polished prose. These are pacing choices, not proof of real disagreement: they can change the texture of dialogue, while distinct goals and positions provide its substance.

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How to apply the design ideas to your own AI debate

  1. Define two positions. Write down what each speaker wants to argue and the assumptions behind that position. Avoid giving both characters the same goal of reaching a balanced conclusion.
  2. Keep the sides distinct. Give each speaker its own context and character sheet, including voice and rhetorical habits. Avoid relying on a single generic instruction to make the speakers “argue.”
  3. Control the turn order in your application. Request one side’s statement, then pass its transcript to the other side as the claim it should answer. Continue alternating as needed instead of expecting an unstructured conversation to police its own roles.
  4. Add pacing deliberately. Use interruptions or unfinished thoughts only where they suit the speakers and scene. Randomly roughening dialogue can make it less polished without making the disagreement more substantive.
  5. Check whether positions persist. Review whether each speaker continues to argue from its assigned goals and assumptions, and whether replies address the other side’s actual points. The account offers no validated scoring method or guaranteed result, so treat this as a practical check rather than a proven evaluation.
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What this account does—and does not—establish

El Otmani’s account offers a set of design dimensions: shared or separate context, prompting or external turn orchestration, generic or differentiated character construction, and uninterrupted or deliberately shaped pacing. It does not report a controlled comparison showing which dimension matters most, or establish that the techniques reliably improve debate quality across models and tasks. The source is Ahmed El Otmani’s DEV Community article, “AI Is Bad at Disagreeing. I Spent Weeks Trying to Fix That.” Its search result lists April 19 and an April 24 edit but does not establish a publication year; the page itself could not be opened in the available retrieval.

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