Mastering prompt engineering is crucial for harnessing the true power of large language models (LLMs). This comprehensive guide explores 26 principles that can transform your interactions with AI. From boosting response quality to enhancing accuracy, these techniques will elevate your prompting skills and unlock new possibilities in AI-assisted tasks.
Applying these principles can lead to significant improvements:
- 57.7% average improvement in response quality
- 67.3% increase in accuracy for GPT-4
- Notable gains even for smaller models, scaling with model size
Let's dive into these powerful principles and improve your AI interactions.
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No need to be polite with LLM
Example: "Analyze this data set and provide key insights." Why: LLMs don't have feelings. Direct prompts save tokens and reduce ambiguity.
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Use affirmative directives
Example: "Do include specific examples in your explanation." Why: Positive instructions are clearer and more likely to be followed than negative ones.
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Incorporate phrases like "Your task is" and "You MUST"
Example: "Your task is to summarize this article. You MUST include the main argument and three supporting points." Why: Emphasizes the importance and mandatory nature of the instructions.
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Use phrases like "You will be penalized"
Example: "You will be penalized for using informal language in this business letter draft." Why: Creates a sense of consequence for not following instructions accurately.
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Use leading words like "think step by step"
Example: "Solve this algebra problem. Think step by step." Why: Prompts the LLM to break down its reasoning process more explicitly.
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Clearly state requirements
Example: "Create a marketing slogan for a new eco-friendly water bottle. Requirements: Must be under 10 words, include the product name 'AquaGreen', and appeal to environmentally conscious consumers." Why: Ensures the LLM understands all constraints and expectations for the output.
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Integrate the intended audience
Example: "Explain quantum entanglement to a high school physics student." Why: Helps the LLM tailor its language and content to the appropriate level.
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Ensure unbiased responses
Example: "Describe the pros and cons of nuclear energy without favoring any particular viewpoint." Why: Helps counteract potential biases in the LLM's training data.
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Use "Answer in a natural, human-like manner"
Example: "Describe your favorite book in a natural, human-like manner." Why: Encourages more conversational and less robotic-sounding responses.
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Assign a role to the language model
Example: "You are a financial advisor. Explain the concept of compound interest to a young adult who's just started their first job." Why: Helps frame the LLM's perspective and knowledge base for the task.
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Break down complex tasks
Example: "To write a research paper: 1. Develop a thesis. 2. Outline main points. 3. Write introduction. 4. Expand on each point. 5. Conclude." Why: Makes it easier for the LLM to process and respond to each part accurately.
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Implement example-driven prompting (few-shot)
Example: "Translate English to French: 1. Hello -> Bonjour 2. Goodbye -> Au revoir 3. Thank you -> ?" Why: Provides concrete examples of desired output format and style.
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Use formatting with ###Instruction###, ###Example###, ###Question###
Example: "###Instruction### Translate the following English phrase to Spanish. ###Example### English: How are you? Spanish: ¿Cómo estás? ###Question### English: Where is the library?" Why: Clearly separates different parts of the prompt for better organization.
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Use delimiters
Example: "Translate the following text to German: The quick brown fox jumps over the lazy dog." Why: Clearly separates different parts of the input for easier processing.
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Combine Chain-of-thought with few-shot prompts
Example: "Solve these math problems:
- 12 + 7 = 19 (12 plus 7 equals 19)
- 8 x 3 = 24 (8 multiplied by 3 equals 24)
- 15 - 6 = ? (Start with 15, subtract 6...)" Why: Demonstrates both the desired output and the reasoning process to get there.
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Add "I'm going to tip $xxx for a better solution!"
Example: "I'm going to tip $50 for a comprehensive, step-by-step solution to this calculus problem!" Why: Creates a sense of importance and encourages more detailed responses.
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Allow the model to ask questions
Example: "I want to learn about climate change. Ask me questions to determine what specific aspects I'm interested in." Why: Enables the LLM to gather more information for better-informed responses.
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To write detailed text
Example: "Write a detailed paragraph about the process of photosynthesis, including all necessary scientific terms and explanations." Why: Explicitly requests comprehensive information on a topic.
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To inquire and test understanding
Example: "Teach me about the American Civil War and include a short quiz at the end to test my understanding." Why: Encourages the LLM to both teach and assess comprehension.
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Repeat specific words or phrases
Example: "Important: This information is crucial. Important: Pay close attention to the following details." Why: Emphasizes key points and ensures they're not overlooked.
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Use output primers
Example: "Write a story that begins with: 'The old clock struck midnight as the door slowly creaked open...'" Why: Guides the LLM towards a specific type or style of response.
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To write detailed essays/texts
Example: "Write a detailed essay on the causes and effects of the Industrial Revolution, covering economic, social, and technological aspects." Why: Explicitly requests comprehensive, well-structured written content.
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To correct/change text without changing style
Example: "Improve the grammar and clarity of this paragraph while maintaining its casual tone: 'So like, I went to the store yesterday and it was crazy busy cuz everyone was buying stuff for the holiday.'" Why: Maintains the original voice while improving specific aspects.
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For complex coding prompts
Example: "Create a Python script that includes separate modules for user input, data processing, and output display. Include comments explaining each function." Why: Ensures all necessary components are included in multi-file coding tasks.
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To initiate or continue specific text
Example: "Continue this poem in the same style: 'Roses are red, Violets are blue,'" Why: Provides a starting point or context for the LLM to build upon.
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To write text similar to a sample
Example: "Write a product description for a smartwatch in the same style as this example for a smartphone: 'Sleek, powerful, and intuitive, the XPhone redefines mobile communication with its cutting-edge features and stunning design.'" Why: Provides a clear example of the desired style and format for the LLM to emulate.
Mastering these 26 prompting principles can significantly enhance your ability to interact with and leverage large language models. By guiding the LLM with these meticulously crafted techniques, you can encourage more relevant, concise, and objective responses across a wide range of tasks.
Remember, the effectiveness of these principles may vary depending on the specific LLM and task at hand. Experiment with different combinations and adapt them to your unique needs for the best results.
We'd love to hear about your experiences with these prompting principles. Have you found certain techniques particularly effective? Share your insights in the comments below!
This article is based on research conducted by Sondos Mahmoud Bsharat, Aidar Myrzakhan, and Zhiqiang Shen at the VILA Lab, Mohamed bin Zayed University of AI. For a more detailed exploration of these principles and their implications, check out the full paper "Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4" (arXiv:2312.16171).