Multimodeling: Use Multiple Chats to Clarify
When one model confuses you, ask another to explain.
The Problem
You ask ChatGPT to explain quantum entanglement. It gives you three paragraphs of dense explanation. You read it twice and still don't get it. Now what?
Most people do one of two things: (1) give up, or (2) say "explain it simpler" in the same chat. But there's a third option: start a new chat (or use a different model) and say "explain this to me: [paste the confusing response]."
This is multimodeling: using multiple chats or models to understand, verify, or improve responses.
Watch: Multimodeling in Action
Same concept, different framing. The second model used analogies you could understand.
Why This Works
When you say "explain it simpler" in the same chat, the model is still reading its own previous explanation. It's anchored to that framing. But when you paste that explanation into a new chat (or a different model), you get a fresh perspective with zero baggage.
Same chat: "Explain it simpler" → model simplifies its own explanation → still uses the same framing you didn't understand
New chat: "Explain this: [paste]" → model treats it as raw input → explains from scratch with different analogies and framing
Use Cases for Multimodeling
Clarification
Model A's response is too complex or uses jargon you don't know
Verification
Model A said something that seems wrong or you want to fact-check
Alternative Framing
Model A's explanation didn't click for you
Model Switching
Your prompt works in Claude but not ChatGPT (or vice versa)
Learning
You want to deeply understand a concept
The Workflow
Model Personalities Matter
Different models have different strengths. This is useful for multimodeling:
The generalist. Good at explaining things in multiple ways. Fast, reliable.
Better at nuanced explanations, writing, and code. More "thoughtful" responses. Less likely to sound like a chatbot.
Strong at search integration and factual lookups. Good for verification.
Cheap and fast. Good for quick clarifications when you don't want to burn through your paid model's rate limit.
Pro tip: Use a cheap model to explain an expensive model's answer. Opus gives you a dense, detailed response? Paste it into Copilot (free) and ask for an ELI5 version. You don't care if you hit Copilot's rate limit.
Real Examples
Claude gave a confusing code explanation
ChatGPT's answer seems wrong
You don't understand Opus's detailed response
Your prompt failed in ChatGPT
Need to verify a fact
When to Use Multimodeling
Multimodeling is useful when:
The response is confusing. Don't fight it in the same chat—get a fresh explanation.
You need verification. Cross-check important answers between models.
The model seems stuck. Same prompt failing in ChatGPT? Try Claude.
You want a different perspective. Claude's explanation is too formal? Get ChatGPT's casual version.
You're learning a concept. Read multiple explanations from different models to build a complete mental model.
When NOT to Use It
Skip multimodeling when:
The answer is good enough. Don't over-optimize. If you got what you needed, stop.
The task is trivial. "Translate to Spanish" doesn't need multi-model verification.
You're just exploring. If you're just chatting and learning, one model is fine.
The Lazy Version
You don't need a special workflow. The lazy version:
- Model A gives a confusing response
- Copy the confusing response
- Open Model B (or new chat in same model)
- Paste and say "explain this to me"
- Model B explains it better
- Now you understand. Done.
That's it. Multimodeling is just "when stuck, get a second opinion."
Claude's note: Different models have different training data, architectures, and post-training. When you ask me to explain ChatGPT's response, I'm not "understanding" it—I'm predicting what tokens would constitute a good explanation of that text. But because my training differs from ChatGPT's, the patterns I invoke will be different. Sometimes that difference is exactly what you need. Sometimes my explanation is worse. The only way to know is to try both and see which framing clicks for you.
Module Complete
This concludes Module 2: Prompting. You now know how to get what you want out of these models: roleplay, examples, meta-prompting, lazy iteration, and multimodeling. Next up: understanding why chats go sideways and how to recover.