I've noticed that a prompt that works perfectly in ChatGPT fails miserably in Claude or Gemini. Why is there such a discrepancy? Is there a "universal language" for Prompt Engineering that works across all LLMs, or do we have to learn a new set of rules for every single model that comes out?
3 answers
Every model is trained on a different dataset and uses different "System Instructions," which is why Prompt Engineering isn't a one-size-fits-all solution. For instance, Claude tends to be more verbose and follows safety guidelines strictly, so you might need to be more encouraging or explicit about the "Persona." GPT-4 is generally better at following complex logic but can be lazy with long outputs. While there is no universal language, a few "Golden Rules" apply: clarity, context, and constraint. If you master these three pillars of Prompt Engineering, you'll only need minor tweaks—like changing the order of instructions—to get consistent results across different platforms.
Allison, that's a sharp observation. Have you noticed if "XML tagging" (like using <task></task>) is a more universal way to handle Prompt Engineering across these different models?
I've found that being polite actually helps with some models but not others. It's weird how much "psychology" goes into effective Prompt Engineering these days!
Haha, it is strange! Some researchers even suggest that telling the AI "this is very important for my career" can improve performance—it’s called "emotional stimulus" in Prompt Engineering circles.
Yes! XML tags are surprisingly effective, especially for Claude and newer GPT models. It helps the model's "attention mechanism" distinguish between instructions, context, and user data. It’s one of the best cross-model Prompt Engineering hacks out there for structured data.