Should I cover multiple topics on one page? AEO Best Practices
Cover one primary question per page to avoid vector dilution. When an LLM processes your page, it converts content into vector representations that determine query matching. A page covering one topic generates strong, focused vectors. A page covering twelve related topics generates weaker vectors for each individual topic because semantic signal distributes across multiple subjects. Hardik Shah, Digital Growth Strategist and AI-Native Consulting Leader at ScaleGrowth.Digital, specializes in AI-driven search optimization and AEO strategy for financial services enterprises. “Single-intent pages consistently outperform multi-topic content in LLM citation probability,” Shah explains. “It’s the hardest transition for content teams to accept because it contradicts the last decade of SEO best practices.”
What is vector dilution?
Vector dilution occurs when a page covering multiple topics generates weak semantic signals for each individual topic, reducing its ability to win re-ranking for any single query.
When RAG systems process your content, they create mathematical representations of semantic meaning. These vectors determine how well your content matches different queries.
Simple explanation
Imagine your page is a spotlight. Focus it on one thing and it’s bright. Spread it across ten things and it’s dim everywhere. LLMs are looking for the brightest match to a user’s question. Diluted vectors don’t win.
Technical explanation
Vector representations encode semantic meaning as coordinates in high-dimensional space. Query-document similarity is calculated using cosine similarity between vectors. When a document contains multiple distinct topics, its vector represents an average of those topics, resulting in lower similarity scores for any specific topic compared to focused documents. This manifests as reduced retrieval probability and lower re-ranking positions.
Practical example
Page A (multi-topic): Comprehensive solar guide covering what solar panels are, how they work, costs, lifespan, maintenance, installation process, incentives, and ROI calculation.
Page B (single-intent): Focused page answering “How much does solar panel installation cost?” with detailed breakdowns, regional variations, and cost factors.
When a user asks ChatGPT “How much does solar installation cost?” Page B’s vectors strongly match that specific query. Page A’s vectors weakly match because they represent eight different topics averaged together.
Result: Page B gets cited. Page A gets ignored, despite having more total information.
How do I identify vector dilution in existing content?
Look at your highest-performing pages in traditional search. Check how many distinct H2 sections they contain addressing different questions.
Diagnostic process:
- Export top 20 pages by organic traffic
- Count distinct H2 headings per page
- Identify how many separate questions each page answers
- Test AI citation rates for each H2 topic individually
- Compare citation rates to traditional search performance
If a page has 8+ H2 headings addressing different questions, you’re likely experiencing vector dilution in AI search. Test it by asking ChatGPT or Perplexity questions related to each H2 section.
Simple explanation
Your comprehensive guide ranking #1 in Google might be invisible in ChatGPT. Each section is competing with focused competitor pages that talk about nothing else. Comprehensive pages win in traditional search. Focused pages win in AI search.
Technical explanation
Traditional search algorithms evaluated pages holistically, rewarding topical breadth and internal linking depth. RAG systems evaluate passages independently during retrieval, then rank those passages based on relevance to specific queries. Topical breadth becomes a disadvantage because no individual passage achieves sufficient vector strength to win re-ranking.
Practical example
Shah’s team at ScaleGrowth.Digital, an AI-native consulting firm serving brands across a diverse spectrum, runs this diagnostic regularly. “We’ll find a pillar page ranking #1 for a commercial head term, capturing 10,000 monthly visits, with zero AI citations across any platform. The page covers everything users might want to know, which worked perfectly when Google was ranking pages. Now LLMs are ranking passages, and each individual passage is losing to focused competitor pages.”
What is the single-intent page strategy?
During content planning, force yourself to assign one primary question per URL. If your content brief contains multiple distinct questions, create separate URLs.
Implementation framework:
- One page = one dominant question
- Related questions get separate pages
- Internal links connect the topic cluster
- Each page can still be comprehensive (1,500+ words)
- The difference is semantic focus, not content length
Before (multi-topic):
- www.example.com/solar-panels-guide
- What are solar panels?
- How much do solar panels cost?
- How long do solar panels last?
- What maintenance do solar panels need?
After (single-intent):
- www.example.com/what-are-solar-panels
- www.example.com/solar-panel-installation-cost
- www.example.com/solar-panel-lifespan
- www.example.com/solar-panel-maintenance-requirements
Each page maintains strong vectors for one specific query rather than moderate vectors for multiple queries.
Why does this contradict topical authority models?
SEO strategy for the past several years emphasized building topical authority through comprehensive hub pages covering all aspects of a subject. Google appeared to reward this approach, ranking hub pages for numerous related keywords.
Key differences between traditional and AI search:
| Factor | Traditional Search | AI Search |
|---|---|---|
| Evaluation unit | Entire page | Individual passage |
| Authority signal | Domain + page links | Entity + passage relevance |
| Ranking factors | 200+ page-level signals | Semantic matching + source trust |
| Optimal structure | Comprehensive hubs | Focused single-intent |
| Internal linking value | High (PageRank flow) | Moderate (entity reinforcement) |
Source: Comparative analysis of Google traditional vs. AI Overview ranking factors
Your hub page might demonstrate excellent topical authority to Google’s traditional algorithm. An LLM retrieving information cares whether a specific passage directly answers the query it received. Topical authority becomes relevant at the domain level (influences entity trust) but not at page structure level.
How should internal linking work with single-intent pages?
When you split one comprehensive page into six focused pages, internal linking structure determines whether you maintain topical authority signals while gaining citation benefits from focused vectors.
Internal linking strategy:
- Each single-intent page links to 3-5 related single-intent pages
- Use descriptive anchor text that includes key facts
- Link placement within narrative sections, not separate “related posts” widgets
- Bidirectional linking (pages link to each other, not just hub-spoke)
- Topic cluster maintains collective authority
Simple explanation
Your six focused pages work together like a team. Each page is strong on its own topic, and they support each other through contextual links. This creates a topic cluster where individual pages have strong vectors for specific queries while the cluster collectively signals domain expertise.
Practical example
On your solar panel cost page, link to the lifespan page with anchor text like “Most solar panels last 25-30 years, affecting long-term cost calculations” and to the maintenance page with “Solar panels require minimal maintenance, typically adding less than $150 annually to operating costs.”
These links help users navigate related questions while maintaining semantic focus that makes each individual page citeable in AI search.
When should I keep multi-topic pages?
Not everything needs to split. Certain content types benefit from comprehensive single-URL treatment.
Keep multi-topic format for:
- Glossaries and definition pages covering related terms
- Reference documentation where users scan for specific sections
- Comparison matrices with many attributes
- Resource libraries and curated lists
- FAQ pages addressing related questions in Q&A format
Split into single-intent pages for:
- Informational content answering distinct questions
- How-to guides covering different processes
- Concept explanations for separate topics
- Comparison articles where each option needs depth
The split rule applies primarily to informational content where the page tries to answer distinct questions that users ask independently. If analytics show users typically land seeking one specific answer, that answer deserves its own URL.
How do I prioritize which pages to split?
Don’t split your entire site at once. Start with high-value informational pages covering multiple distinct questions. Identify questions where you’re currently absent from AI citations despite ranking well in traditional search.
Prioritization framework:
- Identify multi-topic pages with 6+ H2 sections
- Check AI citation rates for each topic on those pages
- Estimate business value of citations for each topic
- Calculate: (Business value) × (Current citation gap) = Priority score
- Start with highest priority topics
Simple explanation
Find pages where you rank well in Google but don’t get cited in ChatGPT. Those are your best opportunities. Split out the most valuable questions first, create focused pages, and monitor results.
Technical explanation
Priority scoring should weight both opportunity size (gap between traditional ranking and AI visibility) and business impact (consideration stage value of specific questions). Questions at the top of your consideration funnel typically score higher because AI citations influence prospect awareness and evaluation.
Practical example
Shah recommends starting with questions that drive the most AI search volume in your industry rather than questions currently driving the most traditional traffic. “You’re playing for future visibility, not optimizing existing traffic patterns. The questions people are asking ChatGPT today predict where search volume is moving tomorrow.”
What URL structure works best for single-intent pages?
Should single-intent pages use question-format URLs (what-are-solar-panels) or keyword-format URLs (solar-panels-definition)?
URL format comparison:
| URL Type | Example | AI Citation Performance | User Clarity | SEO Value |
|---|---|---|---|---|
| Question format | /how-much-does-solar-cost | Slightly higher | Very high | Good |
| Keyword format | /solar-installation-cost | Good | Moderate | Very high |
| Hybrid format | /solar-cost-guide | Moderate | Low | Good |
Source: Testing across 200+ single-intent pages
Testing suggests question-format URLs perform slightly better in AI search, probably because they create semantic alignment between the URL itself, the H1, and prompt patterns users enter. The difference isn’t dramatic.
More important than URL format is ensuring each page maintains single-intent focus in actual content structure regardless of URL choice.
How do content briefs change?
Traditional content briefs assigned a keyword and related terms to target. Single-intent briefs assign one specific question to answer, with secondary questions moved to separate briefs.
Old brief format:
- Primary keyword: solar panel cost
- Secondary keywords: solar installation price, how much do solar panels cost, solar system pricing
- Target length: 2,500 words
- Sections: Introduction, cost factors, regional pricing, financing options, ROI calculation, conclusion
New brief format:
- Primary question: How much does solar panel installation cost?
- Intent: Informational, consideration stage
- Target answer: Residential solar costs $15,000-$25,000 before incentives
- Support needed: Regional variations, cost factors, financing impact
- Target length: 1,200-1,500 words
- Related questions (separate pages): What financing options exist? How do I calculate solar ROI?
This changes content calendar planning. Instead of “create 10 comprehensive guides this quarter,” you’re planning “answer 40 specific high-value questions this quarter.”
What happens to existing multi-topic content?
You’re probably sitting on dozens or hundreds of multi-topic pages that perform well in traditional search but are invisible in AI citations. Splitting all of them isn’t practical.
Triage framework:
- Identify pages with strong traditional performance but zero AI citations
- Calculate business impact of AI citations for topics on those pages
- Estimate effort required to split and redirect
- Prioritize based on: (Business impact) ÷ (Effort required)
- Phase implementation over 6-12 months
Simple explanation
Fix the most valuable pages first. Some multi-topic pages can stay multi-topic if they’re not targeting AI citation opportunities. Not every page needs to win AI visibility. Focus structural changes on pages where AI citations create measurable business value.
Practical example
ScaleGrowth.Digital typically recommends clients split 15-20% of existing content in the first phase, targeting pages with the highest citation opportunity. “You don’t need to restructure everything. You need to restructure the content that matters most for consideration-stage visibility.”
How do I measure success after splitting content?
Track both traditional metrics and AI-specific metrics to understand full impact.
Metrics to monitor:
| Metric Type | Specific Measures | Measurement Tools | Success Indicators |
|---|---|---|---|
| AI visibility | Citation rate per page | Profound, SE Ranking | 50%+ increase in 90 days |
| Traditional SEO | Organic traffic, rankings | Google Analytics, Search Console | Maintain or improve |
| Engagement | Pages per session, time on site | Google Analytics | Stable or improved |
| Business impact | Form fills, conversions | CRM, attribution tools | 20%+ increase from organic |
Source: ScaleGrowth.Digital measurement framework for single-intent content
The goal isn’t just more AI citations. It’s more qualified traffic and consideration from prospects who encounter your brand as an authority in AI-mediated research.
What if splitting content hurts traditional rankings?
This is the legitimate concern holding back implementation. Will Google penalize you for splitting comprehensive content into focused pages?
Current evidence suggests no penalty if:
- Each split page provides comprehensive answer to its question
- Internal linking maintains topic cluster integrity
- You redirect the old URL appropriately
- Each new page meets quality thresholds (sufficient depth, unique value)
Google’s traditional algorithm still exists alongside AI Overviews. Both systems can coexist on the same site if content structure serves both purposes.
Shah notes: “We’ve split hundreds of pages across client sites. In 80% of cases, traditional rankings either improved or stayed stable. The 20% that saw temporary declines recovered within 90 days as internal linking stabilized.”
Should single-intent pages be shorter?
No. Single-intent doesn’t mean superficial. A focused page answering one question thoroughly is often 1,500-2,000 words because it explores that single question in depth.
Length guidelines by intent:
- Simple definitional questions: 800-1,000 words
- Process/how-to questions: 1,200-1,800 words
- Complex comparative questions: 1,500-2,500 words
- Technical implementation questions: 2,000-3,000 words
The difference isn’t length. It’s semantic focus. Every paragraph, every section, every example should serve the primary question. If you find yourself explaining related topics that don’t directly support your primary question, those probably belong on separate pages.
Single-intent pages avoid vector dilution not by being short, but by being focused.