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Capital Letters

Use CAPITAL LETTERS strategically to highlight the most important parts of your request. This helps focus the model’s attention on what matters most.

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Tones

Specify exactly how you want the response delivered:
  • Firm and decisive
  • Confident and authoritative
  • Creative and poetic
  • Story-driven narrative
  • Business professional
  • Technical and detailed
  • Light and humorous
  • Academic rigor
  • Sales-focused
  • Formal presentation
  • Casual conversation
  • Warm and friendly

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Famous People / Experts

When you need specialized expertise, reference specific well-known professionals in that field. Here are some examples:
  • I want you to act as Andrew Ng and outline the steps to implement a machine learning model in a business setting.
  • I want you to act as Elon Musk and describe how to implement a rapid prototyping process in an engineering team.
  • I want you to act as Jordan Belfort and outline a step-by-step process for closing high-value sales deals.
  • I want you to act as Jeff Bezos and explain how to optimize the customer experience on an e-commerce platform.
  • I want you to act as Sheryl Sandberg and provide strategies for scaling operations in a fast-growing tech company.
  • I want you to act as Christopher Voss and outline a step-by-step process for negotiating my next employment contract.

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Use Positive Instructions When Possible

State the behavior you want the model to follow. Keep explicit negative constraints for hard boundaries, such as content the model must never reveal. Prompt with negation:
Don't talk about any other baseball team besides the New York Yankees. Prompt without negation:
Only talk about the New York Yankees.

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Ask LLMs for Direct Quotes

LLMs are probabilistic algorithms that generate the next token based on previous input. As we covered in our Basics of AI models guide, this probabilistic approach means they can sometimes generate responses that aren’t factually accurate, even when they sound convincing. This is called hallucination. We recommend always verifying generated responses. The most effective way to catch hallucinations when working with your data is to ask for direct quotes. This forces the model to provide specific excerpts rather than generating plausible-sounding but potentially inaccurate information.

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The Limit Per Response

We discussed context window lengths earlier, but there’s another important constraint: response limits. The limit per response is the maximum number of tokens a model can generate in one response. The limit varies by model. Here’s the thing: you can work around this! When you hit the limit, just prompt the model to continue:
  • Continue
  • Go on…
  • And then?
  • More…
Pro tip: Instead of pushing for very long responses, break your content into chunks. Ask for “Part 1: Introduction and Problem Definition” in one prompt, then “Part 2: Solution Analysis” in the next. You’ll get better, more focused content without the repetition risk that comes with extended generation.