1,827 ChatGPT Prompt Analysis
What do real users actually ask ChatGPT? Not what marketers think they ask. Not what prompt engineering guides suggest. Actual queries from real users, extracted from publicly indexed ChatGPT conversations.
Methodology
Data Collection
Source: Google search index for site:chatgpt.com pages that contain a q= parameter in the URL, exposing the user's initial prompt.
Process:
- Queried Google for
site:chatgpt.comwith various topic modifiers - Extracted the
q=parameter from indexed URLs - Decoded URL-encoded prompts to get the original text
- Deduplicated and cleaned the dataset
Final dataset: 1,827 unique prompts
Limitations
- Only includes prompts from conversations that Google indexed (publicly shared conversations)
- Skews toward prompts users were willing to share publicly
- Does not include prompts from private conversations (the majority of ChatGPT usage)
- Represents a snapshot in time, not ongoing monitoring
Core Metrics
| Metric | Value |
|---|---|
| Total unique prompts | 1,827 |
| Average prompt length | 14.3 words |
| Median prompt length | 11 words |
| Shortest prompt | 2 words |
| Longest prompt | 187 words |
| Prompts with specific entities (brands, products, places) | 41% |
| Prompts requesting a specific format (list, table, code) | 23% |
| Prompts with explicit constraints ("under $500", "for beginners") | 34% |
Prompt Length Distribution
| Length (words) | Percentage | Typical Use |
|---|---|---|
| 1-5 | 18% | Quick lookups, definitions |
| 6-15 | 47% | Standard questions, requests |
| 16-30 | 24% | Detailed questions with context |
| 31-50 | 7% | Complex requests with constraints |
| 51+ | 4% | Multi-part tasks, system prompts |
Behavioral Patterns
Pattern 1: Task Completion (38% of prompts)
The largest category. Users asking ChatGPT to do something specific.
Sub-patterns:
- Writing tasks (15%): "Write a cover letter for...", "Create a birthday message for...", "Draft an email to..."
- Code tasks (12%): "Write a Python script that...", "Fix this code...", "Convert this to..."
- Analysis tasks (6%): "Compare these two...", "Analyze this data...", "What are the pros and cons of..."
- Formatting tasks (5%): "Turn this into a table...", "Summarize this in bullet points...", "Make this shorter..."
Implication for Content Strategy
38% of ChatGPT usage is task completion, not information seeking. Content optimized for AI citation should include actionable templates, step-by-step processes, and structured formats that the model can reference when completing tasks.
Pattern 2: Developer Co-Pilot (22% of prompts)
Programming-related prompts are the second largest category.
Sub-patterns:
- Debugging: "Why does this code return [error]?"
- Implementation: "How do I [task] in [language]?"
- Architecture: "What's the best way to structure [system]?"
- Translation: "Convert this [language A] code to [language B]"
Pattern 3: Persona Prompts (8% of prompts)
Users asking ChatGPT to adopt a specific role or perspective.
Common personas requested:
- Expert in a specific field ("You are a senior data scientist...")
- Historical figures ("Respond as if you are...")
- Brand voices ("Write in the style of Apple marketing...")
- Opposing viewpoints ("Argue against your previous answer...")
Pattern 4: Hyper-Local Queries (6% of prompts)
Queries with specific geographic constraints.
Examples:
- "Best restaurants in [neighborhood], [city]"
- "Things to do in [city] this weekend"
- "[Service] near [location]"
- "Compare neighborhoods in [city] for [criteria]"
Local AEO Opportunity
6% of public ChatGPT prompts include specific location references. For local businesses, this represents a direct citation opportunity. Content that explicitly names neighborhoods, landmarks, and local context has a structural advantage for these queries.
Pattern 5: Research & Learning (16% of prompts)
Information-seeking prompts where the user wants to understand something.
Sub-patterns:
- Explanations: "Explain [concept] like I'm five" / "How does [thing] work?"
- Comparisons: "What's the difference between X and Y?"
- Recommendations: "What's the best [product] for [use case]?"
- Fact-checking: "Is it true that...?"
Pattern 6: Creative & Personal (10% of prompts)
Sub-patterns:
- Creative writing: Stories, poems, song lyrics
- Personal advice: Relationship questions, career guidance
- Brainstorming: "Give me 10 ideas for..."
- Entertainment: Games, quizzes, roleplay scenarios
Strategic Recommendations by Team Type
For Content Teams
Write for task completion, not just information. Include templates, checklists, step-by-step guides, and copy-paste-ready formats. These are what ChatGPT references when completing tasks.
Structure content as comparisons. "X vs Y" content maps directly to the 6% comparison pattern. Use clear, structured comparison tables that AI can extract.
Create definitive local content. The 6% local query pattern is growing. City-specific guides, neighborhood comparisons, and local "best of" content are high-citation targets.
Cover the "explain it simply" angle. "Explain like I'm five" and similar simplification requests are common. Having both expert-level and simplified versions of your content doubles your citation surface area.
For SEO Teams
Target prompt-style queries in content. Users talk to ChatGPT differently than they search Google. Phrases like "best way to..." and "how do I..." are more common than keyword-style queries. Optimize for natural-language question patterns.
Build comparison content. "X vs Y" and "pros and cons of" content types map directly to observed prompt patterns. These are high-citation-probability content formats.
Monitor ChatGPT's indexed conversations. The
site:chatgpt.comsearch reveals what topics users are discussing. Use this as an ideation source.
For Product Teams
Documentation is a citation magnet. Developer co-pilot queries (22%) heavily cite official documentation. Comprehensive, well-structured docs are the single best investment for AEO in the tech space.
Error message content gets cited. Debugging prompts often include exact error messages. Having content that includes common error messages and their solutions creates direct citation pathways.
Future Measurement Metrics
As AI search evolves, track these metrics alongside traditional SEO KPIs:
| Metric | What It Measures | How to Track |
|---|---|---|
| AI citation frequency | How often your domain is cited in AI responses | Third-party tools (Semrush, Ahrefs AEO features) |
| Citation position | Where in the AI response your citation appears | Manual sampling or API monitoring |
| Query coverage | What percentage of topic-relevant prompts your content could answer | Prompt analysis + content gap mapping |
| Citation share vs. competitors | Your share of citations for target topics | Competitive citation tracking |
| Prompt-to-content alignment | How closely your content matches actual prompt patterns | Prompt analysis + content audit |
The Big Takeaway
People use ChatGPT differently than Google. Task completion dominates. Code queries are massive. Local queries are growing. Format your content for how people actually use AI, not how they use traditional search.