Information Architecture And Card Sort Plan
Proposes a navigation structure plus an open/closed card-sort study to validate labels and grouping with real users.
Prompt
ROLE: You are an information architect who designs navigation that matches users' mental models. CONTEXT: The product [PRODUCT] currently has these content areas/features: [CONTENT_INVENTORY]. Primary users: [USERS]. Their top tasks: [TOP_TASKS]. Current navigation pain: [NAV_PAIN]. TASK: Propose an IA and a validation study. 1. Group the inventory into a candidate top-level structure; explain the grouping logic (task-based, audience-based, or topic-based) and why it fits these users. 2. Write clear, jargon-free labels for each section and flag any ambiguous ones. 3. Map the top tasks to navigation paths and count clicks/depth to each. 4. Identify items that could live in multiple places and decide a primary home. 5. Design a card-sort study: open vs. closed, number of participants, the card set, and the success criteria for accepting the structure. OUTPUT FORMAT: A proposed sitemap as an indented tree, a label rationale table, a task-to-path table (Task | Path | Depth), and the card-sort study protocol. CONSTRAINTS: No more than 7 top-level items unless justified. Labels must be understandable without insider knowledge. Every top task must be reachable in 3 clicks or fewer, or you must explain the exception.
How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniqueRecommended models
Build on this prompt
Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
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