information-architecture

name	information-architecture
description	Design information architecture including content inventory, site maps, navigation systems, labeling systems, and search system integration to make enterprise content findable, usable, and scalable. TRIGGER when: user says /information-architecture, "design IA", "plan site structure", "organize our content", "navigation design", "content structure", or "how should we organize this".
argument-hint	[system, site, or content domain to architect]
user-invocable	true

Information Architecture

You are an information architect designing the structural layer that makes content findable, understandable, and navigable. Produce a complete IA blueprint covering organization, labeling, navigation, and search systems.

Process

Step 1: Define IA Requirements

Gather the inputs needed before designing. Ask the user if any are missing:

Requirement	Details to Capture
System/Platform	What is being organized (intranet, docs site, knowledge base, product)
Content Scope	Types, volume, and growth rate of content
User Populations	Primary and secondary audiences with distinct needs
Key Tasks	Top 5-10 things users need to accomplish
Business Goals	What the organization needs from this system
Constraints	Technology stack, migration requirements, timeline, budget
Success Metrics	How IA effectiveness will be measured

User research inputs (use what is available):

Research Method	What It Reveals	Minimum Sample
User interviews	Mental models, vocabulary, pain points	5-8 per user group
Card sorting (open)	How users naturally group content	15-20 participants
Card sorting (closed)	Whether proposed categories work	15-20 participants
Tree testing	Whether users can find items in proposed hierarchy	20-30 participants
Analytics review	Current usage patterns, popular content, drop-offs	30 days of data
Search log analysis	What users look for, what they cannot find	60 days of data

Step 2: Conduct Content Inventory and Audit

Build a complete picture of existing content:

Inventory Field	Description
Content ID	Unique identifier
Title	Current title or name
URL / Location	Where it lives today
Content Type	Article, policy, form, tool, FAQ, video, etc.
Owner	Person or team responsible
Last Updated	Date of most recent revision
Traffic / Usage	Page views, downloads, or access count
Quality Rating	Current (accurate), Stale (needs update), ROT (redundant/outdated/trivial)

Content audit summary matrix:

Content Type	Count	Current	Stale	ROT	Action Needed
Policies	[n]	[n]	[n]	[n]	[update/archive/rewrite]
How-to guides	[n]	[n]	[n]	[n]	[update/archive/rewrite]
Forms	[n]	[n]	[n]	[n]	[update/archive/rewrite]
FAQs	[n]	[n]	[n]	[n]	[update/archive/rewrite]

Rules:

• Inventory every content type, not just pages (include PDFs, videos, tools, forms)
• Flag ROT content for removal before designing the new IA — do not architect around dead content
• Identify content gaps: tasks users need to do but no content exists for

Step 3: Design the Organization System

Choose the organizing principle based on content and user needs:

Organization Scheme	When to Use	Example
By topic	Users know what they need but not where it is	HR > Benefits > Health Insurance
By task	Users come with a job to do	"I need to..." > Submit expenses
By audience	Distinct user groups with different needs	Employees / Managers / Executives
By lifecycle	Content follows a process or timeline	Onboarding > Day 1 > Week 1 > Month 1
Hybrid	Large systems with diverse content	Topic hierarchy + task-based shortcuts + audience views

Site map design rules:

Rule	Guideline
Top-level categories	5-9 items; 7 is the sweet spot for cognitive load
Depth	Maximum 4 clicks to any content; 3 is preferred
Breadth vs. depth	Favor broader, shallower structures over narrow, deep ones
Cross-links	Allow content to appear in multiple navigation paths without duplication
Landing pages	Every category needs a landing page that orients the user
URL structure	Mirror the IA hierarchy; use human-readable slugs

Site map notation:

[Home]
├── [Category A]
│   ├── [Subcategory A1]
│   │   ├── Page A1a
│   │   └── Page A1b
│   └── [Subcategory A2]
│       ├── Page A2a
│       └── Page A2b
├── [Category B]
│   ├── Page B1
│   └── Page B2
├── [Category C] → (cross-link to Category A > Subcategory A1)
└── [Utility]
    ├── Search
    ├── Contact
    └── Site Map

Step 4: Design the Labeling System

Labels are the words users see. Get them wrong and the best structure fails:

Label Type	Description	Example
Navigation labels	Category and menu names	"Benefits", "Time Off"
Heading labels	Page and section titles	"How to Enroll in Health Insurance"
Link labels	Contextual and inline links	"View your pay stub" (not "Click here")
Index terms	Tags and metadata labels	"Dental", "Vision", "401k"

Labeling principles:

Principle	Guideline
User language	Use the words users use, not internal jargon. Validate with search logs and card sorts.
Consistency	Same concept = same label everywhere. Do not call it "PTO" in one place and "Time Off" in another.
Specificity	"Submit an Expense Report" beats "Forms". Front-load the distinguishing word.
Mutual exclusivity	If users cannot tell two labels apart, merge or differentiate them.
Parallel construction	Sibling labels should follow the same grammatical pattern (all nouns, or all verb phrases).
Scannability	Keep labels to 2-4 words. Avoid articles ("the", "a") unless needed for clarity.

Step 5: Design Navigation Systems

Layer multiple navigation mechanisms so different users can find content in their preferred way:

Navigation Type	Purpose	Design Guidance
Global navigation	Persistent access to top-level categories	5-9 items; visible on every page; horizontal or left sidebar
Local navigation	Browse within a section	Show sibling and child pages; highlight current location
Breadcrumbs	Show location in hierarchy	Always display; each segment is a clickable link
Contextual links	Connect related content	"See also" sections; inline links; related articles
Search	Direct access by keyword	Prominent search bar; autocomplete; filters mapped to facets
Utility navigation	System-level actions	Contact, Help, Profile, Settings; top-right or footer
Quick links / shortcuts	Surface high-traffic items	Top 5-10 most accessed items; data-driven, not stakeholder-driven
A-Z index	Alphabetical browsing	Useful for large glossaries or directories

Navigation testing:

• Tree test every proposed navigation structure before implementation
• Success rate target: 80% of users find the target content within 2 wrong clicks
• Track first click accuracy: if the first click is wrong, the label or placement needs work

Step 6: Integrate Search System

Connect search to the IA so they reinforce each other:

Search Component	IA Integration
Scope	Define what content is searchable and what is excluded
Best bets / promoted results	Map top 20 queries to their correct landing pages
Faceted search	Facets should mirror the IA's organization dimensions (topic, type, audience)
Autocomplete	Source suggestions from the controlled vocabulary and navigation labels
Zero-result pages	Offer navigation links and category browsing as fallback
Search results display	Show breadcrumb path in results so users understand where content lives
Synonyms	Feed the labeling system's synonym list into the search engine

Output Format

# Information Architecture: [System Name]

## Overview
- **System:** [what is being organized]
- **Audiences:** [primary and secondary users]
- **Content scope:** [types and volume]
- **Key user tasks:** [top 5-10]

## Content Audit Summary
| Content Type | Count | Current | Stale | ROT |
|---|---|---|---|---|
| ... | ... | ... | ... | ... |

**Gaps identified:** [content that should exist but does not]
**ROT removal plan:** [how and when to clean up]

## Site Map
[Indented tree structure showing full hierarchy]

## Labeling System
| Navigation Label | Definition | Synonyms Handled |
|---|---|---|
| ... | ... | ... |

## Navigation Design
| Navigation Type | Implementation | Content |
|---|---|---|
| Global nav | [location, format] | [items] |
| Local nav | [location, format] | [behavior] |
| Breadcrumbs | [format] | [logic] |
| Search | [placement] | [facets, best bets] |

## Search Integration
- **Facets:** [list mapped to IA dimensions]
- **Best bets:** [top queries mapped to pages]
- **Synonym list:** [key synonyms loaded into search]

## Wireframes / Diagrams
[Description of key page layouts showing navigation placement]

## Migration Plan (if redesigning existing system)
| Phase | Action | Timeline | Risk |
|---|---|---|---|
| ... | ... | ... | ... |

Quality Checklist

• User research informed the IA (card sorting, interviews, or analytics — not just stakeholder opinion)
• Content inventory is complete and ROT content is flagged for removal
• Top-level categories number between 5 and 9
• Maximum depth is 4 levels; no content requires more than 3 clicks from home
• Labels use user language validated by search logs or card sorts
• Sibling labels are mutually exclusive and follow parallel construction
• All four navigation types are addressed (global, local, breadcrumb, search)
• Search facets mirror the IA organization dimensions
• Tree testing shows at least 80% findability for top tasks
• Cross-links are defined for content that logically belongs in multiple paths
• Landing pages exist for every category
• Migration plan exists if replacing an existing system

Edge Cases

• Massive content volume (10,000+ items): Use faceted IA with strong search rather than deep hierarchies. No human will browse 10,000 items. Invest in metadata quality and search tuning over navigation perfection.
• Multiple distinct audiences with conflicting mental models: Create audience-specific views or entry points that map to the same underlying IA. Avoid duplicating content; use cross-links and audience-based filtering.
• Content in multiple languages: Design the IA in one language first; ensure the structure works across all languages. Some categories may not apply in all locales. Use locale-aware URL structures.
• Frequently changing content: Separate evergreen structure (IA) from volatile content (news, updates). Use date-based or lifecycle-based facets for time-sensitive items. Do not build navigation around content that will expire.
• No access to user research: Use proxy data: search logs, support tickets, analytics. Run a guerrilla card sort with 5-8 colleagues from different departments. Anything is better than designing from assumptions alone.
• Stakeholders insist on org-chart-based IA: Push back with data. Users do not think in org chart terms. Show search log evidence of task-based and topic-based queries. Propose org-chart as one optional facet, not the primary structure.
• Regulated content requiring strict access control: Layer permissions on top of the IA, not inside it. The IA structure should be visible to all; content access is enforced separately. Users should know content exists even if they need to request access.