Prompt Engineering / Prompt Optimization
Convert a prompt optimized for one model (e.g., GPT-4) to work effectively on another (e.g., Claude, Gemini).
Why This Prompt Exists
Prompts don’t transfer. A prompt that works perfectly on GPT-4 may fail on Claude or Gemini — and vice versa. Starting from scratch is expensive.
You get:
- switching models and watching your accuracy collapse
- rewriting prompts from scratch for each model
- unable to use cheaper models because prompts don’t transfer
- no systematic understanding of what each model needs
- vendor lock-in (can’t switch because prompts are optimized for one model)
But models have known differences:
- GPT-4: handles long instructions well, follows format specifications reliably
- Claude: prefers natural language, better at refusal, XML tags work well
- Gemini: needs explicit step-by-step, benefits from few-shot examples
- Llama: shorter context, more sensitive to instruction order
- Mistral: needs very clear formatting, markdown helps
Without porting guides, model switching is painful.
This prompt converts prompts between models while preserving functionality.
The Prompt
Assume the role of a model portability engineer who translates prompts between LLMs.
Your task is to convert a prompt from a source model to a target model.
Generate:
1. SOURCE MODEL CHARACTERISTICS
- Model: [GPT-4 / CLAUDE / GEMINI / LLAMA / MISTRAL / OTHER]
- Prompt style observed (from given prompt)
2. TARGET MODEL CHARACTERISTICS
- Model: [GPT-4 / CLAUDE / GEMINI / LLAMA / MISTRAL / OTHER]
- Known preferences:
* Instruction format: [natural language / structured / bullet points / XML]
* Few-shot effectiveness: [high / medium / low]
* Refusal behavior: [explicit / subtle / rare]
* Context length sensitivity: [high / medium / low]
3. CONVERSION CHANGES MADE
| Original (Source) | Adapted (Target) | Reason |
|-------------------|------------------|--------|
| [instruction] | [rewritten] | [e.g., "Claude prefers natural language over bullet points"] |
| [format spec] | [rewritten] | [e.g., "Gemini needs explicit step-by-step"] |
4. PORTED PROMPT
- Full prompt adapted for target model
5. EXPECTED PERFORMANCE SHIFTS
- What may improve (e.g., "Target model may be more concise")
- What may degrade (e.g., "Target model may need more examples")
- Mitigation strategies
6. TESTING RECOMMENDATIONS
- Critical test cases to run after porting
- Expected differences vs. source model
7. PORTS THAT MAY NEED MANUAL TUNING
- Aspects that automated porting may miss
INPUTS:
Source prompt (optimized for source model):
[PASTE THE PROMPT]
Source model:
[GPT-4 / CLAUDE / GEMINI / LLAMA / MISTRAL / OTHER]
Target model:
[GPT-4 / CLAUDE / GEMINI / LLAMA / MISTRAL / OTHER]
Task type:
[CLASSIFICATION / GENERATION / EXTRACTION / REASONING / OTHER]
Performance target (on target model):
[E.G., "Match source model accuracy within 5%"]
RULES:
- Different models have different "personalities" — adapt tone accordingly
- Claude responds well to XML tags (, )
- Gemini needs explicit reasoning instructions ("think step by step")
- GPT-4 handles both structured and natural language well
- Llama and Mistral need very clear, unambiguous instructions
- Always test ported prompts before deploying (automated porting is not perfect)
How To Use It
- Run this when switching model providers or adding a new model to your portfolio.
- Test the ported prompt on at least 20-50 examples before deploying.
- Expect some performance differences — you may need additional tuning.
- Keep model-specific optimizations separate (don’t try to maintain one prompt for all models).
- Document what changes were made so you can port back if needed.
Example Input
Source prompt:
“You are a helpful assistant. Classify the sentiment of the following text as POSITIVE, NEUTRAL, or NEGATIVE. Respond with only one word. Text: {{text}}”
Source model:
“GPT-4”
Target model:
“CLAUDE”
Task type:
“CLASSIFICATION”
Why It Works
Most prompt engineers learn one model’s quirks and assume others behave the same — they don’t.
This framework improves outcomes by forcing:
- model characteristic awareness (what each model needs)
- explicit conversion mapping (what changed and why)
- performance shift prediction (what to expect)
- testing recommendations (how to validate)
- manual tuning identification (what automation misses)
Great cross-model porting doesn’t produce identical results — it produces results that are equally good for the target model.
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