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Published on 05.02.2026
TLDR: This guide walks you through creating an iPhone Shortcut that captures your AI prompting goals, constraints, and desired outputs, then automatically generates structured prompts and saves them to a searchable Notes library, eliminating the need to rewrite prompts or scroll through chat history.
Summary:
One of the most frustrating patterns in AI usage is prompt amnesia. You craft a brilliant prompt that solves a specific problem perfectly, and weeks later when you need it again, it's buried somewhere in chat history, lost forever in the scrolling void. This article addresses that exact pain point by teaching you how to build an intelligent system right on your iPhone that captures, structures, and preserves your prompts for future reuse.
The core insight here is deceptively simple but powerful: the best prompts aren't spontaneous; they're structured. They contain three essential elements - a clear goal, explicit constraints, and a defined output format. By capturing these three components through a series of questions, you can generate consistent, effective prompts that work reliably across different AI models. The shortcut acts as a template engine, turning your thinking process into structured data that an AI can then transform into a production-ready prompt.
The workflow is elegantly divided into four phases. First, you're prompted for information - your goal (like summarizing a meeting), your constraints (professional tone, under 150 words), and your desired output format (bullet points organized by type). Second, the shortcut passes these inputs to either Apple Intelligence or ChatGPT, which synthesizes them into a complete prompt with a descriptive title and purpose statement. Third, the output is formatted with markdown to ensure consistency and readability, with the purpose in bold and the actual prompt in a copyable code block. Fourth, everything gets saved to a dedicated Notes folder, fully searchable and organized.
For teams and architects, this pattern is worth understanding at a deeper level. This isn't just about personal productivity; it's about standardizing how AI is used within an organization. If you can get teams to adopt a structured approach to prompt engineering - capturing goals, constraints, and outputs consistently - you've solved a significant operational problem. You've made AI usage auditable, repeatable, and improvable. Different team members can build on each other's prompts, and you can start to see patterns in what works and what doesn't. This scales the intelligence of your team's AI adoption from ad-hoc experimentation to systematic practice.
There's also an interesting technical detail worth noting: the distinction between Apple Intelligence's dictionary output and ChatGPT's text output. Apple Intelligence can return structured data (title, purpose, prompt as separate fields), while the standard ChatGPT action returns conversational text. This difference matters because structured output is what enables the markdown formatting and consistent organization. If you're forced to use ChatGPT, you lose some of that structure and formatting benefit, but the core concept still works.
Key takeaways: