How to Prompt Across Models Without Pretending They Work the Same

I can carry a task from one model to another, but I should not assume every provider handles instructions, context, tools, or output constraints identically. A shared task brief is a starting point; the test result decides what to change. Before blaming the provider, Compare Two Prompt Versions Without Fooling Yourself shows how to compare versions on unchanged inputs.

For a code review, I keep the same diff, intended behavior, risk checklist, and acceptance tests. Those are my requirements. Then I check the provider’s current documentation for any model-specific guidance rather than relying on an old comparison table. OpenAI’s model guidance, Google’s Gemini prompt strategies, and Anthropic’s Claude prompting guide are better starting points for current behavior than a static “universal prompt” claim.

I compare whether each model follows the requested format, handles the boundary case, and flags unknowns. If one misses a rule, I adjust the instruction or use a provider feature such as structured output when appropriate. I do not claim one model is “best” from one example.

Save the date, model name, task, prompt version, and test results. Models and guidance change. A note without those conditions becomes misleading quickly. Try it: Run one non-sensitive prompt on two models. Keep input and rubric identical. Record the differences, then make one provider-specific change and rerun only the failed case. Keep learning: Compare Two Prompt Versions Without Fooling Yourself, Structured Outputs: When a Schema Beats More Prompt Words, and How to Test a Prompt on More Than One Example.