Write prompts in plain, explicit language without relying on model-specific tricks — and test every prompt in each model you plan to use, because the same words can produce noticeably different results in Claude versus GPT-4 versus Gemini.
Why Models Respond Differently to the Same Prompt
Different AI models are trained on different data with different alignment techniques. Claude tends to follow explicit instructions very literally and is cautious about edge cases. GPT-4 tends to be more conversational and will sometimes go beyond what you asked. Gemini has its own patterns. This isn’t a quality difference — it’s a personality difference, like two excellent teachers who run their classrooms very differently. A lesson plan written for one doesn’t automatically land the same way with the other.
If you deploy the same system prompt across multiple models without testing, you may get consistent behavior on the easy questions and very inconsistent behavior on the edge cases — which are exactly the situations where consistency matters most.
How to Write for Portability
The safest approach for cross-model prompts is to rely on clarity over cleverness. Use short, direct sentences. State each rule once, explicitly. Avoid “jailbreak-proofing” techniques that are specific to one model’s quirks — they often break on other models. Name the specific behaviors you want rather than describing the outcome you’re hoping for. “Do not use bullet points in your response” is portable. “Write in a flowing, conversational style” is interpreted differently by each model.
Test with at least five diverse questions before relying on any cross-model prompt in a live setting. Include at least one tricky or off-topic question to see how each model handles the edge case.
What This Means for Educators
Most educators building a campus agent will use one model consistently — usually Claude or GPT — and portability is a non-issue. But if your students access your agent through different tools, or you switch platforms down the road, a portable prompt means less rework. Writing explicit, plain-language prompts is good practice regardless — it makes your agent more predictable on the platform you’re using today and easier to move tomorrow.
The Simple Rule
Write prompts that a smart new employee could follow on their first day — clear, explicit, no assumed context. That kind of prompt works reliably across models, across updates, and across time. Clever tricks break; clarity endures.