Picture this. You have a solid draft sitting in ChatGPT. You copy it over to Claude because you want a tighter, more structured version. Then you spend the next five minutes staring at the prompt field, trying to figure out what to type.
Not because you need new information. Just because you want a different version of what you already have.
A Redditor in r/ChatGPTPromptGenius just described this exact trap, and their fix is the kind of thing that sounds obvious until you realize you have never actually done it: stop prompting AI for answers, and start prompting it for transformations.
🤔 The Two-Model Trap Nobody Talks About
Here is how most multi-model workflows actually play out. You get a solid result from one model. You move to the other for polish or structure. Then instead of treating it like a continuation, you start over. You write a fresh prompt, rethink the ask, try to rebuild the context from scratch.
The original poster kept hitting this wall with a specific rhythm: get a draft in ChatGPT, switch to Claude for structure or polish, then freeze up trying to figure out how to “ask it the right way.”
The core problem is not the prompt. It is the framing. When you switch models, you default to question mode: “what should I ask next?” But the smarter question is “what do I want this to become?” That shift sounds minor. It changes everything about how you use two models together.
🎯 Why This Friction Is Costing You
Most people do not fully leverage multi-model setups because the switching friction cancels out the benefit. You spend so much time re-explaining and re-prompting that you might as well have stayed in one model the whole time.
The transformation framing removes that friction almost entirely. You already have output. You do not need to regenerate anything from scratch. You just need to route what you have through a different engine with a clear instruction about what it should become.
Each model has different strengths. ChatGPT might be better for raw generation in some use cases. Claude might be better for structure, tone, or detail work. The transformation pattern lets you stack those strengths without resetting the conversation every time you switch models.
🔧 How to Do It
The author shared a base template that works across both models. Here it is exactly as posted:
“Take the following and transform it into [desired format]. Keep the core meaning, but improve structure, clarity, and tone. Here is the content: [PASTE OUTPUT]”
The workflow is four steps:
- Generate your initial content in whichever model you start with.
- Copy the full output.
- Open the second model and paste the transformation prompt above, filling in what you want it to become.
- Drop your copied content at the end and send.
No new context to rebuild. No re-explaining the goal. You are routing content, not restarting a conversation.
💡 Four Transformation Prompts Worth Bookmarking
The creator shared several ready-to-use versions for common situations. Here are the ones that cover most of what people actually need:
Clean up a rough draft:
“Rewrite this to be concise, structured, and easy to read. Remove repetition.”
Turn scattered notes into something usable:
“Convert this into a clear, step-by-step plan with sections.”
Make something shareable:
“Turn this into a polished version I could send to someone or post.”
These are not clever prompts. They are slots. Drop your output in, get a better version out. The value is how little thinking each one requires once you have it saved somewhere.
A few tips worth adding to these:
- Name your transformations. Once you know “clean up,” “convert notes,” and “make shareable” reliably work, save them in a doc you can paste from. They become a repeatable toolkit instead of one-off experiments.
- Be specific about the target format. “Transform into a step-by-step guide” lands better than “improve this.” The clearer the destination, the better the output.
- Keep the base prompt minimal. The original poster’s template is short on purpose. Format, meaning, clarity, tone. That is enough. Adding twenty constraints usually makes things worse.
👇 Give This One Try Today
Grab something you have already generated this week and run it through one of those transformation prompts. Start with the “clean up” version if you are not sure which to pick. Compare what comes back to the original. That is the whole exercise.
I tried this within an hour of reading the post, and it genuinely cut down the friction I had been accepting as normal. Once you see it work once, you stop writing fresh prompts every time you switch models and you start thinking in transformations instead.
The full thread is worth a read if you regularly switch between ChatGPT and Claude. Head to r/ChatGPTPromptGenius and find the post from u/Last-Bluejay-4443 to see the full breakdown and any follow-up variations people shared in the comments.
Frequently Asked Questions
Q: Do I really need to rewrite my prompt for Claude if I already have a working version for ChatGPT?
Not usually. Instead of re-prompting from scratch, try sending your existing output to Claude with a transformation request, like “rewrite this to be more concise” or “restructure this as step-by-step instructions.” You’re leveraging what already works rather than starting the prompting process over, which saves time and reduces friction.
Q: What if my first draft is pretty rough, will transformation prompts still help?
Transformation works best when you already have decent foundational content (maybe 70% there). If your initial output is way off-base, a direct rewrite for the specific model might be faster. But if it just needs restructuring, polish, or tone adjustments, transformation is a major time-saver.
Q: What transformation prompts should I keep ready to use?
A few high-leverage ones: “Rewrite this to be concise, structured, and easy to read, remove repetition”; “Convert this into a clear, step-by-step plan with sections”; “Turn this into a polished version I could send to someone or post.” You can adapt and reuse these across different AI models, which builds consistency without overthinking each prompt.
Q: Can I automate this workflow instead of copy-pasting between models?
Yes. The post’s author built Threadmark (a Chrome extension) to save outputs and apply transformations across ChatGPT and Claude quickly. If you do this regularly, a tool like this, or even a custom command in your chat interface, can eliminate the manual copy-paste friction.
Stop rewriting prompts between ChatGPT and Claude. Do this instead
by u/Last-Bluejay-4443 in ChatGPTPromptGenius