Prompt Engineering for LLMs
Help me write a prompt for [real task] using principles that should work reasonably well across different large language models. Prioritizing portable principles over model-specific tricks matters if you switch between tools regularly.
Prompt engineering principles that transfer across different LLMs — specificity, structure, examples — work more reliably than model-specific tricks, since the underlying language-model behavior these principles address is genuinely shared across most current large language models.
What this produces
A prompt built from cross-model-portable principles, guidance on which techniques tend to transfer well versus which are more model-specific, and a translation approach for moving a working prompt from one LLM to another.
Variations
Three ready-to-copy versions for cross-model prompt engineering.
Help me write a prompt for {real task} using principles that should work reasonably well across different large language models — real specificity, real structure, real examples — not model-specific tricks.
This prompt worked well on {real model A}: {paste real prompt}. I’m switching to {real model B}. What real adjustments, if any, are worth trying?
Here’s my real prompt: {paste real prompt}. Does this rely on anything genuinely model-specific, or is it built on principles that should transfer reasonably well?
FAQ
Do all LLMs respond identically to the same prompt?
No — while core principles transfer reasonably well, exact behavior and quality can vary meaningfully between models, so verify on your real actual target model.
How do I know if a technique is portable or model-specific?
Techniques based on general language and structure clarity tend to be portable; techniques exploiting a specific model’s particular training or interface tend to be model-specific.
Should I maintain separate prompts for each model I use?
For prompts where quality matters significantly, testing and adjusting per model is worth the effort, even starting from a shared portable base.
Does prompt length portability vary by model?
Context length limits vary meaningfully by model — verify your real specific model’s current limits if working with long prompts.
