How to Prompt Different AI Models (and Why the Same Prompt Doesn't Work Everywhere)

If you've ever copied a prompt that worked great in one AI tool into another and gotten a worse answer, you're not imagining it. In our guide to understanding the different AI models, we covered which model is strongest for which job. This post covers the other half of the equation: once you've picked a model, how do you actually talk to it well?

TL;DR: Structure and clarity help on every model, but each lab tunes its model to respond best to a slightly different style. Claude tends to reward explicit structure and step-by-step reasoning requests. GPT responds well to role-based framing and iterative refinement. Gemini benefits from being explicit about the format you want back. Grok does best with direct, conversational instructions. Open-weight models generally need more explicit instructions since they've had less fine-tuning polish.

Why prompts aren't universal

Every model is trained on a different mix of data, fine-tuned with different techniques, and optimized for different default behaviors. Some are tuned to be terse by default; others are tuned to add caveats and structure unless told otherwise. A prompt written for one model is really a prompt written for that model's specific training, and moving it to another model without adjustment is one of the most common reasons people conclude a model is "worse" when it's actually just being spoken to in the wrong dialect.

Prompting proprietary models

• GPT (OpenAI): responds well to role-based framing ("act as a senior editor") and to being given a clear goal plus constraints up front. It also handles iterative, back-and-forth refinement well, so treating a first draft as a starting point rather than a final answer tends to produce better results. OpenAI's own prompting guide is a good reference for their latest recommendations.
• Claude (Anthropic): tends to reward explicit structure. Using clearly labeled sections, numbered steps, and XML-style tags to separate instructions from content noticeably improves consistency, especially on longer or more complex tasks. Anthropic's prompt engineering documentation covers this in detail, including how to ask for step-by-step reasoning.
• Gemini (Google): does well when you're explicit about the output format you want, whether that's a table, a specific word count, or a particular tone, since it will otherwise default to a fairly verbose, general-purpose style. It's also worth being explicit when you want it to reason through a problem versus just answer directly.
• Grok (xAI): tends to do best with direct, plainly worded instructions rather than heavily formatted prompts, and its access to real-time information means it's worth explicitly asking it to check current sources when freshness matters.

Prompting open-weight models

Open-weight models like Llama, DeepSeek, Qwen, GLM, and Kimi generally need more explicit instructions than the top proprietary models, since they typically have less fine-tuning polish layered on top of the base model. Being very specific about format, tone, and constraints matters more here than with a flagship proprietary model, which is more likely to infer what you want from a shorter prompt.

General practices that help almost everywhere

• Be specific about the goal and the audience. "Write a summary" produces a different result than "write a two-sentence summary for a busy executive who has never seen this data before."
• Give examples. Showing one or two examples of the kind of output you want (sometimes called few-shot prompting) is one of the most reliable ways to improve quality on any model.
• Ask for step-by-step reasoning on hard problems. Explicitly asking a model to think through a problem before answering tends to improve accuracy on reasoning-heavy tasks, particularly on models that support extended or visible reasoning.
• Iterate rather than expecting a perfect first draft. Treating the first response as a starting point and asking for specific revisions usually gets you to a better result faster than trying to write the perfect prompt up front.

FAQ

Do I need to write a completely different prompt for every model? Not from scratch, but you should expect to adjust it. A prompt that works on Claude will often work reasonably well on GPT or Gemini with minor tweaks to formatting and directness, rather than needing to be rewritten entirely.

Does prompt engineering still matter as models get smarter? Yes, though the bar has moved. Today's models are more forgiving of vague prompts than earlier generations, but clear, specific instructions still produce meaningfully better results, especially for complex or high-stakes tasks.

What's the single highest-impact thing I can do to improve my prompts? Give the model an example of what a good answer looks like. It's usually more effective than adding more instructions.

Should I use the same prompt style for a "flash" or "mini" model as I do for a flagship model? No. Smaller, faster models generally do better with shorter, more direct prompts, while flagship and reasoning-tuned models can handle (and often benefit from) more detailed, structured instructions.

Last updated: July 27, 2026. Prompting best practices evolve as labs update their models, so check each provider's official documentation for the latest recommendations.