Skip to main content

Prompt Engineering Best Practices

Crafting effective prompts is key to maximizing the performance of Large Language Models (LLMs). Here are some best practices to follow:

1. Be Clear and Specific​

  • Avoid Ambiguity: Use precise language. Instead of "Write about dogs," try "Write a paragraph explaining the historical significance of the Golden Retriever breed as a hunting companion."
  • Define the Task: Clearly state what you want the LLM to do (e.g., summarize, translate, explain, generate code, classify).
  • Specify the Output Format: If you need a specific format (e.g., list, JSON, table, specific tone), explicitly ask for it.

2. Provide Context​

  • Background Information: Give the LLM the necessary context to understand the request. If asking about a specific concept, provide a brief definition or relevant details.
  • Define the Audience: Specify the target audience for the response (e.g., "Explain this concept to a 5-year-old," "Write a technical report for experts").
  • Set the Scene: If relevant, describe the situation or scenario.

3. Use Examples (Few-Shot Prompting)​

  • Demonstrate the Pattern: For complex tasks or specific formats, provide one or more examples (input/output pairs) to show the LLM exactly what you want.
  • Ensure Consistency: Make sure your examples accurately reflect the desired output style and structure.

4. Assign a Role or Persona​

  • Guide the Style and Knowledge: Instruct the LLM to act as a specific expert, character, or persona (e.g., "Act as a world-class chef and suggest a recipe..."). This helps tailor the response's tone, style, and knowledge base.

5. Break Down Complex Tasks​

  • Chain of Thought/Step-by-Step: For multi-step reasoning problems, ask the LLM to think step-by-step or outline its reasoning process. This often leads to more accurate results.
  • Sequential Prompting: Divide a complex task into smaller, sequential prompts. Use the output of one prompt as the input for the next.

6. Refine and Iterate​

  • Experiment: Prompt engineering is often iterative. Don't expect the perfect prompt on the first try.
  • Analyze Outputs: Examine the LLM's responses to understand why a prompt might not be working as expected.
  • Adjust: Modify your prompt based on the analysis. Try different phrasing, add more context, change the instructions, or provide better examples.

7. Consider Constraints and Safety​

  • Set Boundaries: If necessary, specify what the LLM should not do (e.g., "Do not include any personal opinions," "Avoid technical jargon").
  • Responsible AI: Be mindful of potential biases and safety concerns. Avoid prompts that could generate harmful, unethical, or biased content.

By applying these best practices, you can significantly improve the quality, relevance, and accuracy of the responses you receive from LLMs.