Fan-out queries are the background web searches that AI assistants perform to gather information before answering your question. When a user asks ChatGPT a question, the system often executes multiple searches like “best SEO tools comparison 2026” to find sources to cite. Understanding these hidden queries reveals where the real citation opportunities exist.
In this article, we explain how fan-out queries work, share data on which query types drive the most citations, and show how to target them for your GEO strategy.
How do fan-out queries work?
Fan-out queries are web searches LLMs perform during Retrieval-Augmented Generation (RAG). According to research from Google, AI systems break user questions into component searches to gather authoritative sources.
When a user asks “What’s the best SEO tool?”, the system might execute multiple searches:
- “SEO tools comparison 2026”
- “best SEO software features”
- “SEO rank tracking tools”
The pages ranking for these fan-out queries become citation candidates, not the pages ranking for the user’s original question.
Which fan-out query types appear most frequently?
Our tracking of AI responses reveals distinct patterns in query frequency:
| Query Category | Example Query | Frequency Range |
|---|---|---|
| Tool comparisons | ”SEO rank tracking tools” | 70 to 95 occurrences |
| Best lists | ”best tools for optimizing content for AI assistants” | 70 to 75 occurrences |
| Alternative searches | ”best alternatives to [tool] 2025” | 70 to 75 occurrences |
| How-to queries | ”how to measure product mention frequency in AI” | 70 to 80 occurrences |
Source: Superlines fan-out query tracking across 3,827 tracked prompts, December 2025 to January 2026.
How are citations distributed across ranking positions?
The data shows a steep drop-off in citations between positions:
| Position | Avg. Citation Count | Share of Total |
|---|---|---|
| #1 | 786 to 1,573 | 35 to 40% |
| #2 | 716 to 1,460 | 25 to 30% |
| #3 | 708 to 1,407 | 20 to 25% |
| #4 | 143 to 681 | 8 to 15% |
| #5 | 86 to 611 | 5 to 10% |
Position #1 captures 2 to 3 times more citations than positions #4 and #5. The first three positions collectively account for 75 to 85 percent of all citations for a given fan-out query.
Research from Ahrefs confirms this pattern, showing that top-3 results receive the majority of AI citations across query types.
Why do alternative searches distribute citations differently?
When users ask AI about alternatives to specific tools, fan-out queries follow unique patterns. For queries like “best alternatives to [tool] 2025”:
- Multiple content types compete (comparison articles, G2 listings, brand pages)
- Citation distribution is more evenly spread (positions #1 through #5 each receive 15 to 25%)
- Recently updated content (2025 or 2026 dates in titles) ranks higher
In our dataset, Superlines.io ranks #4 for the query “best alternatives to Profound GEO 2025” with 143 citations, demonstrating that mid-ranking positions capture meaningful volume in competitive alternative searches.
How do tool comparison queries differ?
Queries like “SEO rank tracking tools” show different patterns:
- Citation concentration is higher at position #1 (1,573 citations versus 1,284 at #5)
- Niche specialist sites outperform general publications
- Blog content outperforms product pages
Why should GEO target fan-out queries instead of user queries?
Traditional SEO targets user queries
You optimize for “best SEO tools” because users search that phrase.
GEO must also target fan-out queries
AI systems search different queries than users type. A user asks:
“Which tool should I use to track my brand in AI search?”
The AI might search:
“AI search brand monitoring tools comparison”
If your content ranks for the fan-out query, you get cited even though you never optimized for the user’s actual question.
According to Search Engine Land, 79% of content cited in AI responses was updated within the past 12 months, making recency a critical factor for fan-out query rankings.
Which content types perform best for fan-out queries?
| Content Type | Avg. Citation Rate | Best For |
|---|---|---|
| Comparison articles | High | Tool selection queries |
| Product landing pages | Medium | Direct brand queries |
| G2 and review aggregators | Medium to High | Alternative searches |
| Blog tutorials | Medium | How-to queries |
| Documentation | Low to Medium | Technical queries |
How should you build a fan-out query strategy?
Step 1: Map your fan-out landscape
Identify the queries AI systems run in your space:
- Which queries trigger citations in your category
- Current ranking positions for your domain
- Citation volume by position
Step 2: Identify positioning opportunities
For each high-value fan-out query:
- Analyze current top-ranking content structure
- Identify gaps in coverage or recency
- Assess realistic ranking potential
Step 3: Create targeted content
Common fan-out query structures to target:
- “[topic] tools comparison 2026”
- “best [category] software features”
- “[tool] vs [alternative]”
- “alternatives to [competitor]”
- “[topic] statistics and data”
Step 4: Optimize for citation extraction
Content that gets cited contains:
- Complete feature comparisons
- Specific statistics and data
- Clear pros and cons analysis
- Recent update dates visible in title or meta
Are positions #4 and #5 worth targeting?
While position #1 captures the most citations, positions #4 and #5 represent an overlooked opportunity:
- Lower competition: Fewer resources compete for these positions
- Meaningful volume: 8 to 15 percent of citations is significant at scale
- Compound effect: Multiple #4 and #5 rankings across queries adds up
For emerging tools and newer content, targeting positions #3 through #5 may offer better ROI than competing for #1 against established players.
How should you monitor fan-out performance?
Track these metrics:
| Metric | What It Measures | Target |
|---|---|---|
| Ranking position | Where you appear in fan-out results | Top 5 |
| Citation count | How often you are cited from that position | Trending up |
| Query coverage | Percent of relevant fan-out queries you rank for | 20%+ |
| Position stability | Consistency of rankings over time | Low variance |
Key takeaways
- Fan-out queries are background searches that AI systems run to gather information for responses
- Position #1 captures 35 to 40 percent of citations while positions #4 and #5 still capture meaningful volume
- “Alternatives to [tool]” queries distribute citations more evenly across positions
- Comparison content and recent updates improve citation probability significantly
- Targeting mid-ranking positions can offer better ROI for emerging brands
Understanding fan-out queries transforms GEO from guessing which content might get cited to strategically targeting the exact queries AI systems use.
Methodology
This analysis tracked fan-out queries across 3,827 prompts using Superlines from December 2025 to January 2026. Citation counts were aggregated by ranking position across multiple LLM platforms.
Frequently asked questions
What is a fan-out query?
A fan-out query is a background web search that AI assistants perform when answering user questions. Instead of answering from memory alone, LLMs search the web for relevant sources, and the pages ranking for these searches become citation candidates.
How are fan-out queries different from regular search queries?
Fan-out queries are generated by AI systems, not typed by users. They tend to be more specific and comparison-focused than typical user queries. For example, a user might ask “what SEO tool should I use?” while the AI searches “SEO tools comparison features pricing 2026.”
Can I see which fan-out queries AI systems run for my topics?
Yes. AI visibility platforms like Superlines track fan-out queries and show which searches trigger citations in your category, along with current ranking positions and citation volumes.
How important is content freshness for fan-out query rankings?
Very important. Research shows 79% of content cited in AI responses was updated within 12 months. Including current year dates in titles and regularly updating content improves fan-out query rankings.
Should I optimize for fan-out queries or user queries?
Both, but for different purposes. Traditional SEO targeting user queries helps you rank in search engines. GEO targeting fan-out queries helps you get cited in AI responses. The most effective strategy addresses both.