Cross-AI Prompt Translation
Good news: the fundamentals of prompting transfer across every model. If you can prompt Claude well, you can prompt ChatGPT, Gemini, or Llama well. A few surface details differ — here's the Rosetta stone.
What transfers everywhere
These work on all of them — they're about communication, not a vendor:
- Give context: goal, audience, format, tone (Prompting Basics).
- Examples (few-shot).
- Ask for reasoning on hard tasks.
- Constrain the output (length, format, "JSON only").
- Iterate in plain language.
- Roles / system prompts — every major model has the concept (Roles).
Learn these once and you're portable.
What differs (and what to adjust)
| Aspect | Note when you switch |
|---|---|
| Structure cues | Claude loves XML tags; other models accept them but may prefer Markdown headings or JSON. |
| Tone intensity | Older models tolerated forceful/ALL-CAPS prompts; newer literal-followers prefer calm, specific instructions. Re-tune per model. |
| Reasoning | "Think step by step" helps some; dedicated reasoning models do it internally — don't double up. |
| Prefilling | Putting words in the assistant's mouth is a Claude-style lever; support varies elsewhere. |
| System prompt weight | All have one, but how strongly it's followed varies — test. |
| Tool/function calling | Same concept, different request schemas per provider. |
A portable workflow
- Write the prompt with universal structure (context + format + examples).
- Test on your target model with a few real inputs (eval).
- Adjust only the surface details above if needed.
- Keep prompts in version control so switching models is cheap.
:::tip Don't rewrite from scratch Moving to a new model rarely needs a new prompt — usually just a tone/format tweak. Your structure and examples carry over. :::