SUMMARY
Purpose: Information Architecture (IA) is the practice of organising, structuring and labelling content in an effective and sustainable way to support easy navigation and discovery.
Design Thinking Phase: Define
Time: 45–90 min IA workshop + 1–2 days validation and iteration
Difficulty: ⭐⭐
When to use: When designing complex digital products with layered content When users struggle to find information or complete tasks When revamping legacy information-heavy platforms
What it is
Information Architecture (IA) is the foundation of a product’s user experience—it defines how information is grouped, prioritised and labelled, enabling users to navigate your product effectively. It supports findability, usability, and clarity by aligning content structure with mental models and user goals.
📺 Video by NNgroup. Embedded for educational reference.
Why it matters
A strong IA reduces friction, accelerates comprehension, and enhances task success across user flows. Especially in feature-rich products, IA determines whether users find what they need or abandon the experience altogether. Proper IA minimises cognitive load, prevents choice paralysis, and scales well as your product grows.
When to use
- During early-stage design to ensure intuitive structure from day one
- When scaling a product’s content or feature set
- To validate or rework navigation systems after user confusion or drop-off
Benefits
- Rich Insights: Helps uncover user needs that aren’t visible in metrics.
- Flexibility: Works across various project types and timelines.
- User Empathy: Deepens understanding of behaviours and motivations.
How to use it
To implement effective IA, follow these steps:
- Audit existing content: Catalogue all screens, features, and data objects in your current product.
- Conduct card sorting: Use open or closed card sorting with real users to identify meaningful groupings.
- Create a sitemap or diagram: Map navigational structure, establishing parent/child relationships and key paths.
- Label using real language: Avoid jargon. Use labels users actually search for or scan through.
- Validate with tree testing: Assess if users can find information using only the navigation without UI elements.
- Iterate and refine: Align structure with real user expectations uncovered through tests and interviews.
Example Output
For a fictional mental wellness app, your IA output might look like this:
- Top-Level Nav: Home · Daily Check-In · Progress · Library · Profile
- Progress Section:
-
- Overview
- Journal History
- Mood Trends
- Goal Tracking
- Library Section:
-
- Guided Meditations
- Sleep
- Reading List
- Podcasts
Common Pitfalls
- Designer assumptions: Structuring IA based on internal team language instead of user mental models.
- Over-nesting: Burying valuable content under too many layers of navigation.
- Ignoring IA testing: Skipping tree testing or usability validation before launch.
10 Design-Ready AI Prompts for Information Architecture – UX/UI Edition
How These Prompts Work (C.S.I.R. Framework)
Each of the templates below follows the C.S.I.R. method — a proven structure for writing clear, effective prompts that get better results from ChatGPT, Claude, Copilot, or any other LLM.
C.S.I.R. stands for:
- Context: Who you are and the UX situation you're working in
- Specific Info: Key design inputs, tasks, or constraints the AI should consider
- Intent: What you want the AI to help you achieve
- Response Format: The structure or format you want the AI to return (e.g. checklist, table, journey map)
Level up your career with smarter AI prompts. Get templates used by UX leaders — no guesswork, just results. Design faster, research smarter, and ship with confidence. First one’s free. Unlock all 10 by becoming a member.
Prompt Template 1: “Evaluate Navigation Structure with User Goals in Mind:”
Evaluate Navigation Structure with User Goals in Mind:
Context: You are a senior UX designer reviewing a site’s primary nav for a digital health application.
Specific Info: The product serves both first-time users doing symptom checks and returning users tracking health. Current feedback suggests 'Progress' and 'History' are unclear.
Intent: Recommend clearer IA groupings that align with user goals and reduce ambiguity.
Response Format: Provide a table with: Navigation Label · User Goal Supported · Proposed Label · Justification.
If any user types or task goals are unclear, ask clarifying questions before responding.
Then, suggest one follow-up method to validate the revised structure.
Prompt Template 2: “Map an Information Architecture from Card Sorting Results:”
Map an Information Architecture from Card Sorting Results:
Context: You are a UX researcher summarising open card sort results from 30 survey participants.
Specific Info: Participants sorted 40 pieces of content related to a university’s online learning portal.
Intent: Generate a proposed IA model based on dominant groupings and naming conventions.
Response Format: Provide a nested list or outline of sections and subsections with rationales for groupings.
If content types or platform goals are unclear, ask clarifying questions before continuing.
Then, suggest how to test these groupings using tree testing.
Prompt Template 3: “Compare Mental Models Across User Segments:”
Compare Mental Models Across User Segments:
Context: You’re a product designer creating IA for a mobile finance app.
Specific Info: You have behavioural data on novice users vs expert investors navigating budgeting, spending, and reporting tools.
Intent: Highlight IA decision points where segmentation affects structure or labels.
Response Format: Provide side-by-side comparison tables for each segment including: Content Purpose · Novice Model · Expert Model · Recommendation.
If financial tools or user distinctions are unclear, ask clarifying questions first.
Then, recommend one hybrid IA solution to accommodate both segments.
Prompt Template 4: “Design Context-Aware Labels”
Design Context-Aware Labels:
Context: You're updating navigation labels for a multilingual travel booking app.
Specific Info: Users often confuse 'Trips' vs 'Bookings', and machine translation fails to capture context accurately in German and Japanese.
Intent: Generate culturally appropriate, disambiguated nav labels per language setting.
Response Format: Provide a table showing: Original Label · Language · Issue · Recommended Label · Rationale.
If platform scope or key workflows are unclear, ask clarifying questions.
Then, suggest a follow-up test plan to validate comprehension.
Prompt Template 5: “Generate IA Patterns by Industry”
Generate IA Patterns by Industry:
Context: You're a design lead benchmarking IA patterns for a knowledge portal in the climate-tech space.
Specific Info: You’re referencing 10 comparable platforms from gov, NGO, and private sectors.
Intent: Extract common IA patterns including entry points, filters, and topic hierarchies.
Response Format: Return a bullet list grouped by sector: Sector · Entry Pattern · Notable Sections/Filters · Navigation Type.
If the sector scope is too broad, suggest segmenting further.
Then, recommend one example-based sitemap tailored to climate-tech.
Prompt Template 6: “Identify Duplicate or Redundant Sections”
Identify Duplicate or Redundant Sections:
Context: You’re redesigning a B2B SaaS dashboard with 7+ years of iterative feature growth.
Specific Info: IA includes repeated modules labelled 'Reports', 'Insights', and 'Analytics' with overlapping content.
Intent: Reveal redundancies and suggest IA simplifications to reduce confusion.
Response Format: Output a table with: Section Name · Overlapping Content With · Recommendation · User Impact.
Ask for clarification if legacy labels are ambiguous.
Then, propose a plan to communicate changes during the redesign.
Prompt Template 7: “Create IA Personas Based on Content Engagement”
Create IA Personas Based on Content Engagement:
Context: You're structuring a community-driven cooking app with social recipes, videos, and meal diaries.
Specific Info: Analytics show distinct usage patterns among casual browsers, influencers, and returning diarists.
Intent: Develop IA personas reflecting content needs and navigation paths.
Response Format: Provide persona overviews with: Content Used · Nav Priorities · Structural Needs · Suggested Top-Level Labels.
Ask for clarification if behavioural metrics are unclear.
Then, recommend one method to prototype IA quickly for each persona.
Prompt Template 8: “Generate Label Variants for A/B Testing”
Generate Label Variants for A/B Testing:
Context: You're updating top-level navigation on a job matching platform.
Specific Info: You’re testing whether users respond better to action-oriented (‘Apply Now’) vs context (‘Job Listings’) labels.
Intent: Produce strong, testable variants with different intents: emotional, functional, playful.
Response Format: Table with: Current Label · Variant Type · Proposed Label · Intended Effect.
Suggest one method to test these in low-cost usability experiments.
Prompt Template 9: “Build a Voice-First Information Architecture”
Build a Voice-First Information Architecture:
Context: You're designing IA for a smart speaker interface to control household devices.
Specific Info: Commands and context vary (bedroom lights vs outdoor sprinklers), and users speak commands instead of tapping.
Intent: Construct an IA that supports voice loops and flexible phrasing.
Response Format: Provide a node tree that shows voice branches, disambiguation steps, and fallback prompts.
Ask for clarification if device types or use cases aren’t fully detailed.
Prompt Template 10: “Simplify a Deeply Nested IA Tree”
Simplify a Deeply Nested IA Tree:
Context: You're advising a client with a 5-level-deep intranet for 200 departments.
Specific Info: Users complain about lost content and excessive clicks.
Intent: Flatten the structure while preserving comprehension and department ownership.
Response Format: Return a revised sitemap outline showing: Level · Topic Group · Merged/Relabelled? · Recommendation.
If organisational policies restrict hierarchy changes, suggest compromise strategies.
Recommended Tools
- Optimal Workshop – Card Sorting and Tree Testing
- FlowMapp – Visual IA mapping
- FigJam – IA brainstorms and architecture diagrams
- Xmind – Deep IA structure mapping
- UXtweak – Usability testing for navigation flows
