I let 3 LLMs argue on the famous AI "Car wash: Walk or Drive" problem to prove a point.

Hilarious breakdown of the car wash LLM debate! Wild how Gemini flubbed the basic goal logic solo, but cross-model back-and-forth fixes those obvious reasoning gaps instantly. Makes total sense to treat LLMs like a panel of engineers instead of one all-knowing source.

We run multiple models in production at SocialPost.ai and the pattern you found holds: telling model B that model A wrote something reliably makes it more critical, which is a cheap and effective review layer. The business insight buried in your experiment is that disagreement is the product, since consensus answers are what every single-model tool already sells. I'd point the positioning at high-stakes decisions where a second opinion is worth paying for, not riddles.

This is a fun and smart experiment!
Love how you let them duke it out on that classic car wash riddle — the Gemini stubbornness had me cracking up. Really drives home the point about not putting blind trust in one LLM. Solid reminder to cross-check models more often. Nice work!

That DeepSeek quote made me laugh too
I agree with your main point though: for important decisions, using multiple LLMs is often better than relying on a single one. Different models have different strengths, and having them challenge each other's reasoning can reveal mistakes or blind spots.
I treat them more like a team of advisors than a single source of truth.

I especially agree with the first part. Many fellow students of mine always use ai to make all their chores. I believe that we should let our critical thinking control how we use AI, and with that, much more people are going to start seeing posibilities with AI, like for example: how do I start a business? How can I implement AI in a workflow? How can AI make life much easier for those in need? Many people are not confident in AI because they only see the part of what media says, and what's its mostly used for: doing chores, answering basic questions, etc. Good comment, Mr. Abbas.

This mirrors something I've seen building with multiple model APIs: a single model tends to anchor on the most "socially safe" surface-level answer, especially on ambiguous physical-reasoning prompts like this one. Your debate format is basically cheap ensemble reasoning, similar to how early multi-agent setups (e.g. AutoGPT-style critique loops) improved factual accuracy at the cost of latency and token spend. Have you tested whether the order models are introduced biases the final consensus? Gemini went first here and anchored the framing even though it was wrong, so I'm curious if you've measured whether "losing" models actually change their underlying reasoning or just soften their tone without truly updating.

Interesting approach — using multi-LLM debate as a quality check.

My pipeline does the opposite: single LLM call per layer, but with strict scoring formulas on top. I learned that adding a "devil's advocate" pass (e.g., "is this real payment or hypothetical?") catches more bad ideas than letting LLMs argue with each other.

But I'm curious — does the debate approach actually catch the fake demand cases in your experiments? That's been the hardest filter for me.

Really helpful to read this. As a non-technical founder I keep going back and forth on how much to build vs validate first — how did you decide?

The chats always want to please you, which creates a bias. See too many people validating their ideas in Claude and ChatGPT and they tell them exactly what they want to hear. Just opening another session and saying beat this up for me and poke holes in it is interesting to see results each time.

this is exactly right -- the verification layer is the bottleneck, not the generation. we see the same pattern when assessing people's AI skills (aisa.to) -- the gap between casual users and effective ones almost always comes down to whether they have a systematic process for checking output, or whether they just go with "does this sound right." multi-model debate is one of the better approaches I've seen for high-stakes decisions.

I like the idea of using multiple models , but I'd take it one step further : don't just let them agree - assign roles. One model generates , another critiques, a third verifies against sources or tests. The diversity of reasoning is what improves the final answer.

@sharjeelabbas This is a great illustration of something I've run into building multi-agent systems: single-model consensus is often just "confident agreement," not correctness. The interesting part of your setup isn't the debate itself, it's that disagreement forces each model to actually justify its reasoning instead of pattern-matching to the most likely-sounding answer.
One thing I'd be curious about: have you noticed models converging faster on questions with an objectively verifiable answer (math, logic puzzles) vs. ones that are more judgment-based (strategy, prioritization)? In my experience, multi-agent setups add the most value on judgment calls, since verifiable questions don't really need a "debate," just a fact-check. But if debate improves accuracy even on verifiable questions like your car wash example, that's a stronger signal that single-LLM laziness is a bigger problem than people assume.
Also — 7 debate formats is a lot of design work. Which one produces the most disagreement in practice, and which one do you personally trust the most for real decisions?

Man I do this all the time with my gym routine and what to eat. I just ask one app and follow it blindly without thinking twice. Never realized the answers were probably lazy until I read your piece. Gonna start asking different apps and see what they say about each other instead of just picking one and sticking with it.

The interesting part here isn't just getting a better answer, it's surfacing disagreement before people over-trust the first polished response. A useful next layer would be showing where the models split before the final synthesis, because that's usually where the real judgment call lives. I ran into something similar building DictaFlow, the fastest AI cleanup often sounds confident while quietly changing meaning, so we lean hard toward preserving the original words instead of "improving" them. Same idea here, speed is cheap, trust is the product.

Nice article

The car wash example is a perfect illustration of why “LLM as oracle” is dangerous and “LLMs as a panel” is way more robust, especially for non-trivial decisions. Beyond debates, have you explored ways to surface where models diverge most (e.g. highlight the exact assumptions they don’t share) so users can inspect that directly?

What stood out to me isn't that multiple models debate—it's that you're treating disagreement as useful evidence instead of something to eliminate.

A lot of AI products optimize for producing an answer quickly. In higher-stakes decisions, understanding where capable models disagree can be just as valuable as the final answer itself.

I never trusted single LLM they always hallucinate this would improve the trust, good product!

Been doing something adjacent to this on a smaller scale, not multi-model debate, just refusing to trust a single model's verdict on its own output and making myself read the raw result instead of the pass or fail summary. Caught real bugs a first glance would've missed.

Curious about a specific failure mode with debate though, has it ever converged confidently on a wrong answer, all three models agreeing and still being wrong? Confident agreement between models isn't the same as correctness if they share the same blind spot to begin with, seems like the harder case to catch than the one where they disagree and you can actually see it happening.

This is a great point. People often compare LLMs like they’re searching for the “winner,” but the real value might come from combining different perspectives. A debate layer between models is an interesting way to reduce blind spots.

Im a AI beginner, do you have any advice to learn the AI information?

I do this for anything that matters. One model writes it. Another tries to break it. The weak spots show up fast.

This is a great demonstration of why relying on a single LLM is risky. We've been experimenting with multi-agent debates in our own work — having two or more models critique each other's outputs before finalizing a response.

The interesting thing is that even when models agree on the wrong answer (like both picking "walk" when "drive" is the right call), the process of forcing them to justify their reasoning reveals the flaws.

Are you planning to turn this into a tool or API? I could see a "LLM debate" layer being useful for high-stakes decisions.

I do a version of this every day without meaning to, since running two or three coding agents on the same task and watching where they disagree is basically free signal on which one actually reasoned through it versus pattern matched a similar problem. The interesting cases are never the ones where they land on different final answers, it's when they land on the same answer through completely different logic and only one of the two paths would survive a slightly different version of the question. Did you push on that, asking a variant of the car wash problem to see if the agreement held or was coincidence?

I’ve also started making decisions by asking AI for advice, but I’ve begun using several different ones. In your opinion, which LLM is the best?

Feel free to ask any questions I would be happy to answer, a like would go a long way!