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OpenAI Prompt Engineering Cheat Sheet

Overview

This cheat sheet provides guidelines and examples for crafting effective prompts to elicit the best responses from OpenAI's language models, including GPT-3 and GPT-4.

Key Concepts

  • Prompt Engineering: The practice of designing and refining the input given to a language model to achieve the desired output.
  • Tokens: The pieces of text (words or parts of words) that the model processes. Effective prompt design considers token limits.
  • Temperature: A setting that controls the randomness of the model's responses. Lower values make the output more deterministic.

Prompting Techniques

1. Direct Questions

Asking direct questions tends to produce concise and specific answers.

  • Example: ```plaintext What is the capital of France? ``` Expected Output: ```plaintext The capital of France is Paris. ```

2. Instruction Following

Provide clear, detailed instructions for complex tasks.

  • Example: ```plaintext Explain the theory of relativity as if I'm a ten-year-old. ``` Expected Output: ```plaintext The theory of relativity says that the faster you move, the slower time passes for you compared to someone who is not moving. ```

3. Zero-Shot and Few-Shot Learning

Demonstrate a task with examples (few-shot) or without them (zero-shot) to guide the model.

  • Zero-Shot Example: ```plaintext Translate 'Hello, how are you?' into French. ``` Few-Shot Example: ```plaintext English: How are you? French: Comment allez-vous?

    English: I am fine. French: Je vais bien.

    Translate 'Where is the library?' into French. ```

Advanced Prompt Design

Contextual Embedding

Embed the task within a context to guide the model’s tone and style.

  • Example: ```plaintext As a witty travel blogger, describe a visit to the Eiffel Tower. ```

Chain of Thought

Encourage the model to "think aloud" as it approaches problem-solving tasks.

  • Example: ```plaintext You are a detective solving a mystery. Describe your thought process as you determine who took the last cookie. ```

Formatting Tips

  • Bold and Italics: Use **bold** for emphasis and *italics* for less emphasis.
  • Lists: Use - or * for bullet points.
  • Code: Use backticks ` for inline code and triple backticks ``` for blocks of code.
  • Tables: ```markdown
    Task Type Description Example Prompt
    Informational Provides factual information. What is the tallest mountain?
    Instructional Gives directions to perform a task. Describe how to bake a cake.
    ```

Best Practices

  • Be Specific: The more specific the prompt, the more accurate and relevant the output.
  • Provide Context: Include necessary background information to orient the model.
  • Iterative Refinement: Start with a basic prompt, then refine based on the model's output.