Definition
AI Topical Maps are thematic mappings generated using language models to identify all subtopics, questions, and angles of a subject. AI analyzes semantic entities, relationships between topics, and search intents to produce a complete hierarchical structure. This approach enables planning an exhaustive content strategy covering a topic in depth (topical authority). AI topical maps are often used alongside keyword clustering to maximize semantic coverage.
Key Points
- Exhaustive topic mapping by AI
- Identifies subtopics, hierarchies, and semantic relationships
- Foundation of topical authority strategy
Practical Examples
Link building topical map
An LLM generates a complete topical map of link building: link types, acquisition strategies, tools, metrics, risks, 2026 trends, etc., with hierarchical links between subtopics.
Editorial planning
From an AI topical map, the team plans 6 months of articles progressively covering all identified subtopics, from the pillar page to supporting articles.
Frequently Asked Questions
Ask an LLM to list all subtopics, then organize them into hierarchical categories with relationships. Refine iteratively with specific prompts.
It's an excellent starting point but must be validated and enriched by a domain expert. AI may miss industry nuances or very recent trends.
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Last updated: 2026-02-07