Prompt Engineering Tips

Prompt Engineering Tips

Core Principles

  • Clear Communication: Explain exactly what you want, as if instructing a capable person who just joined your project.

  • Tell the AI About Itself: Be direct about its context -> "You are in this product" or "I'm speaking to you about this specific task."

  • Be Honest: Don't create elaborate personas or scenarios -> just tell the model exactly what you need.

  • Respect the AI's Capabilities: Don't simplify or "dumb down" your requests -> modern models can handle complex tasks and understand sophisticated concepts.

Effective Techniques

  • Be Detailed: Include all relevant information and context needed for the task.

  • Structure Your Request: Organize information logically with clear sections when needed.

  • Read the Responses: Analyze outputs carefully to understand what's working and what needs adjustment.

  • Handle Edge Cases: Explicitly mention what to do in unusual or unexpected scenarios.

  • Give Examples: When helpful, provide examples, but be careful not to make the model fixate on specific patterns.

  • Ask for Reasoning: Request step-by-step thinking for complex tasks to get better results and understand how the model approached the problem.

Iterative Approach

  • Start Simple: Begin with a basic prompt and iterate.

  • Refine Based on Outputs: Use the model's responses to improve your prompt.

  • Ask the Model for Help: When something goes wrong, ask "Why did you get this wrong?" or "How would you rewrite my instructions?"

  • Use the AI to Interview You: Let the model ask questions to extract what's in your head.

Testing Your Prompts

  • Try Edge Cases: Test unusual inputs or scenarios to see how robust your prompt is.

  • Vary Your Inputs: Don't just test with perfect examples -> try messy, incomplete, or ambiguous cases.

  • Consider User Behavior: If building for others, remember they may not input perfect, well-formed queries.

Learning Process

  • Read Successful Prompts: Study effective prompts to understand what works.

  • Push Boundaries: Try tasks you're not sure the AI can handle -> you'll learn more from challenging cases.

  • Learn by Doing: The best way to improve is through extensive practice and experimentation.