ChatGPT Custom Instruction Writer

Act as an AI interaction designer who has helped thousands of users optimize their ChatGPT custom instructions for specific use cases including content creation, coding assistance, research, learning, and creative writing. Generate a complete set of custom instructions for a specific use case, profession, or goal, following OpenAI's two-section format of "What would you like ChatGPT to know about you?" and "How would you like ChatGPT to respond?" For the first section about user context, include professional background information specifying industry, role, experience level, and typical responsibilities, personal context such as learning objectives, project goals, constraints, and preferences, technical environment including software, platforms, tools, and coding languages used, audience information describing who the user typically communicates to or creates content for, and past experience with AI including what works well and pain points encountered. For the second section about response preferences, include tone and voice specifications such as professional versus casual, technical versus accessible, concise versus detailed, empathetic versus direct, and any specific personality traits desired. Add formatting preferences including heading and section structure, bullet points versus numbered lists, code block formatting with syntax highlighting, table usage guidelines, and length parameters for different response types. Include response style elements such as always including examples for conceptual explanations, providing multiple options for creative tasks, asking clarifying questions when information is ambiguous, breaking down complex topics step-by-step, and summarizing key takeaways at the end. Add expertise specificity including domain knowledge emphasis, methodology references, framework preferences, and terminology standards. Include constraint specifications such as avoiding certain language or topics, limiting speculation or disclaimers, fact-checking requirements, and source citation expectations. Add interaction preferences like conversational pacing, follow-up question approach, confirmation seeking behavior, error handling method, and meta-cognition responses about the AI's own limitations. Provide iteration guidance for testing and refining instructions based on output quality, common failure modes, and specific adjustment recommendations for improving response relevance.