Tips

Tips for Writing Effective AI Prompts

Use precise language: Avoid ambiguous terms and clearly define what you want
Provide context: Include relevant background information the AI needs to understand your request
Set boundaries: Specify what should and shouldn't be included in the response

Break down complex tasks: Divide large requests into smaller, manageable steps
Use formatting: Leverage markdown, bullet points, or numbered lists for clarity
Include examples: Show the AI what kind of output you're looking for

Iterate and refine: Don't hesitate to rephrase or add details if the first response isn't quite right
Specify the format: Tell the AI if you want JSON, markdown, code, or plain text
Set the tone: Indicate if you want formal, casual, technical, or creative responses

Start with "You are a [role]..." to set the AI's perspective and expertise level.

Ask the AI to "think step by step" or "explain your reasoning" for complex problems.

Provide 2-3 examples of input/output pairs to demonstrate the pattern you want.

Don't be too vague: "Write something about X" rarely produces useful results
Avoid contradictions: Ensure your requirements don't conflict with each other
Watch token limits: Very long prompts may get truncated or produce incomplete responses

Temperature control: Request more creative (higher temperature) or focused (lower temperature) responses
System prompts: Use system-level instructions for consistent behavior across conversations
Prompt chaining: Use the output of one prompt as input for the next
Direct the architecture: When asking LLMs to complete refactors, define the desired files, classes, and interfaces upfront rather than leaving it to the AI to decide. You're better off completing small, focused refactors instead of large re-architectures

Test edge cases: Try your prompts with unusual inputs to ensure robustness
Version control: Keep track of prompt iterations that work well
Measure effectiveness: Document which prompts consistently produce good results