How to Make Prompts Work Across Any LLM

A portable prompt avoids product-specific tricks. It uses clear markdown, explicit roles, useful context, and concrete output formats. That makes it understandable to any capable language model.

Model-specific features can be useful, but they should not be required for the prompt to make sense. If the core instruction is clear, the prompt can travel.

Headings, bullet lists, numbered steps, tables, and bracketed variables are the closest thing to a universal prompt interface. They are readable by humans and models. They also make prompts easier to save, edit, and share.

A model-agnostic prompt should look like a small document. It should not depend on hidden settings or a particular chat product.

Do not write prompts that say use ChatGPT to do this or answer like Claude unless the tool itself is the topic. Write the job instead: analyze, rewrite, compare, summarize, draft, classify, generate, critique, or plan.

When the task is clear, any model can attempt it. You can then compare outputs based on quality rather than whether the prompt was tied to a brand.

Different LLMs may handle the same prompt differently. One may be better at structured writing, another at concise summaries, another at code, another at creative exploration. Portable prompts let you test those strengths without rewriting the task every time.

If consistency matters, tighten the format. If creativity matters, loosen the constraints and ask for multiple directions.

The model landscape changes quickly. A durable prompt library should not be locked to today's tool names. It should preserve the thinking pattern: context, task, constraints, output, review.

That is the philosophy behind SixtySevenSites: prompts as reusable work instructions, not platform-specific incantations.