Prompt Engineering Rules

Introduction

This guide provides best practices for designing, testing, and refining prompts for AI-powered features. It is intended for developers integrating LLMs (like OpenAI, Anthropic, or Gemini) into web applications.

General Principles

• Clarity: Write prompts that are clear, specific, and unambiguous.
• Context: Provide relevant context and examples in the prompt to guide the model.
• Instruction: Use explicit instructions (e.g., “List 5…”, “Summarize the following…”).
• Format: Specify the desired output format (e.g., JSON, Markdown, plain text).
• Iterate: Test and refine prompts based on real outputs and user feedback.

Types of Prompts

• Zero-shot: No examples, just instructions.
• Few-shot: Include 1–3 examples to guide the model.
• Chain-of-thought: Ask the model to reason step-by-step.
• Role-based: Assign a persona or role to the model for more targeted responses.

Testing & Evaluation

• Maintain a prompt library with effective prompts and their results.
• Test prompts with edge cases and real user data.
• Document prompt changes and their impact on results.

Security & Ethics

• Avoid prompts that could elicit sensitive or harmful outputs.
• Sanitize user input before including it in prompts.
• Log and review AI outputs for quality and safety.

References

• https://platform.openai.com/docs/guides/prompt-engineering
• https://anthropic.com/prompt-engineering
• https://www.promptingguide.ai/