🏴‍☠️ Stop trusting one AI

Use this “Combo” trick instead

Author

Captain YAR
December 18, 2025 • Estimated Reading Time: 8 minutes

Trusting a single AI model to handle complex business tasks often leads to average results or hallucinations.

Most people rely on one favorite chatbot for daily work, like writing emails or debugging code. A prompt engineering instructor recently shared a “Combo” tactic on Reddit that highlights why one-tool workflows fail. The method cross-references answers across major AI models, creating a fast, built-in fact-check loop.

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The Power of Adversarial AI
The core idea is to turn AI output into an adversarial process. A single model often tries to be helpful, which can mean agreeing with bad assumptions or filling gaps with plausible-sounding errors. When you introduce a competing answer, the model shifts from pleasing you to evaluating logic, evidence, and inconsistencies.

Telling one AI that another AI disagrees breaks the “agreeable assistant” pattern. It has to compare reasoning, identify gaps, and defend its conclusion. This mirrors peer review, where claims hold up only after others try to challenge them.

Key Insights from the Expert

Orchestrating a Debate Yields Accuracy
The workflow “pits” models against each other using direct comparison prompts like, “Grok says X, but you said Y—who’s right?” This forces deeper reasoning because the model must analyze a counter-argument, not just answer from scratch. The instructor claims this often leads models to catch and correct their own mistakes without human intervention.

Model Bias and Selection Matters
Tool choice is part of the tactic, not an afterthought. The instructor uses a mix of ChatGPT, Claude, and Grok, and says they’ve reduced GPT usage for certain business and marketing work where others performed better. They also tested Gemini 3 but judged it weaker for this workflow, reinforcing the point: don’t be loyal to one brand.

Different models have different strengths, filters, and failure modes. Mixing them helps cover blind spots, especially when accuracy matters. The goal is not to find “the best” model, but to use disagreement to surface errors.

The Rule of Convergence
You also need a stopping rule. The contributor repeats the loop until at least two or three models converge on a similar answer, then asks each to rate confidence (aiming for 9/10 or 10/10). This “wait for convergence” approach resembles ensemble methods in data science, where agreement across independent systems reduces noise.

If models trained differently reach the same conclusion independently, the odds of correctness improve. It won’t guarantee truth, but it filters out many one-off mistakes and “creative” fabrications. Convergence plus critique is the key.

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How to Execute the “Combo” Tactic
If you want to replicate the results the poster reports in sales, marketing, and coding, follow this workflow.

Phase 1: The Broad Cast
Open three top-tier AI tools in separate tabs (the author suggests ChatGPT, Claude, and Grok). Paste the exact same prompt into all three at once. Do not rewrite it, since differences in interpretation are part of what you’re measuring.

Phase 2: The Cross-Examination
Take Model A’s answer and paste it into Model B, then challenge Model B to explain the differences. Use something like:

I asked Grok this same question, and it provided the following answer: [Paste Answer]. Why is your answer different? Who is correct, and what are the specific gaps in the other logic?

Repeat the loop across all models. Feed Claude to ChatGPT, ChatGPT to Grok, and so on.

Phase 3: The Synthesis and Rating
Watch how each model critiques the others, flags weak claims, and revises. Ask each model to produce a refined final answer based on the critiques. Have each one rate its confidence from 1 to 10.

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Phase 4: Final Selection
Compare the revised outputs and proceed only when at least two models agree and confidence is near perfect. Use the consensus version for high-stakes work like production code or a final sales strategy.