General Structure for Good Prompt Design

Use these core prompting principles to get better output from AI:

Be Explicit & Clear: State exactly what you want using direct action verbs ("Generate," "Analyze," "Summarize") rather than vague requests. Avoid assumptions about what the AI knows about your situation. Specify format requirements—if you need bullet points, a paragraph, JSON, or a script, say so explicitly. Specificity helps the AI understand scope, audience, and desired outcome.

Role-Playing: Assign a persona to set the appropriate expertise level and perspective. Examples include "Act as a senior UX designer," "Pretend you're a technical writer explaining this to non-technical stakeholders," or "You are a business consultant advising a startup CEO." The persona shapes how the AI structures its thinking and what it prioritizes.

Chain-of-Thought (CoT): Ask the AI to "think step-by-step" or break down complex tasks into numbered steps. This encourages showing reasoning rather than jumping to conclusions, resulting in more accurate, thorough, and traceable outputs. For example: "Analyze this step-by-step: First, identify the core issue. Second, list three root causes. Third, propose solutions."

Context & Quality: Define the desired tone, depth, format, and specific features needed. Specify constraints like length, audience sophistication level, or technical requirements. Examples: "Explain this in beginner-friendly language with analogies," "Write for an executive audience (under 200 words)," or "Include error handling and code comments."

The Core Problem: Too many prompts are too open-ended. A vague prompt requires the AI to guess your intent and explore a massive solution space. A well-structured prompt narrows focus by including role, action, context, and quality constraints, dramatically improving the chances of getting exactly what you need on the first try.