26 Principles of Good Prompting

In the rapidly evolving field of artificial intelligence, the ability to communicate effectively with large language models (LLMs) is crucial. Crafting precise and impactful prompts can significantly enhance the quality of responses generated by these models. Whether you're an AI enthusiast, a researcher, or a developer, understanding the nuances of good prompting is essential for leveraging the full potential of LLMs.

This comprehensive guide outlines 26 key principles designed to help you create effective prompts. These principles cover a wide range of techniques, from specifying the intended audience and breaking down complex tasks to using affirmative directives and integrating examples. By applying these strategies, you can ensure your prompts are clear, structured, and capable of eliciting the best possible responses from language models.

Dive into these principles to refine your prompting skills and unlock more accurate, relevant, and coherent outputs from your AI interactions.

26 Principles of Good Prompting

1. No need to be polite with LLM: Get straight to the point and avoid unnecessary phrases like “please” or “thank you.”

2. Integrate the intended audience: Specify the audience's level of expertise, e.g., "The audience is an expert in the field."

3. Break down complex tasks: Use a sequence of simpler prompts in an interactive conversation for complex tasks.

4. Use affirmative directives: Employ positive instructions like ‘do’ and avoid negative language like ‘don’t.’

5. Request clarity or deeper understanding: Use prompts like:

- "Explain [specific topic] in simple terms."

- "Explain to me like I’m 11 years old."

- "Explain as if I’m a beginner in [field]."

- "Write the [essay/text/paragraph] using simple English like you’re explaining to a 5-year-old."

6. Incentivize solutions: Mentioning an incentive can sometimes motivate better solutions, e.g., "I’m going to tip $xxx for a better solution!"

7. Example-driven prompting: Use few-shot prompting by providing examples to guide the model.

8. Structured formatting: Start with ‘###Instruction###’ followed by ‘###Example###’ or ‘###Question###’ if relevant. Separate sections with line breaks.

9. Include mandatory phrases: Use “Your task is” and “You MUST” to provide clear instructions.

10. Incorporate penalties: Use phrases like “You will be penalized” to enforce rules.

11. Natural human-like manner: Use the phrase “Answer a question given in a natural human-like manner.”

12. Sequential thinking: Use leading words like “think step by step” to encourage logical progression.

13. Unbiased responses: Add “Ensure that your answer is unbiased and does not rely on stereotypes.”

14. Interactive clarification: Allow the model to ask questions to clarify details until it has enough information.

15. Testing understanding: Use prompts like “Teach me the [theorem/topic/rule] and include a test at the end without answers.”

16. Assign a role: Give the model a specific role, e.g., “You are a knowledgeable tutor in [subject].”

17. Use delimiters: Separate sections of the prompt with clear delimiters for better understanding.

18. Repetition for emphasis: Repeat a specific word or phrase multiple times within a prompt to emphasize its importance.

19. Combine techniques: Use Chain-of-thought (CoT) prompting with few-shot examples.

20. Output primers: Conclude your prompt with the beginning of the desired output.

21. Detailed writing tasks: Use prompts like “Write a detailed [essay/text/paragraph] on [topic] by adding all necessary information.”

22. Preserve style in revisions: Use prompts like “Revise the paragraph to improve grammar and vocabulary without changing the style.”

23. Code generation across files: For complex coding prompts, instruct the model to generate scripts that span multiple files.

24. Continuation prompts: Start a text with specific words, phrases, or sentences and ask the model to continue.

25. Clear requirements: State the requirements clearly in the form of keywords, regulations, hints, or instructions.

26. Style matching: Instruct the model to write a text similar to a provided sample.

These principles help in crafting effective prompts that guide language models to produce accurate, relevant, and coherent outputs.

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