Where Do LLMs Get Their Information? The Complete Source Guide
Understanding where Large Language Models (LLMs) like ChatGPT and Gemini source their information is crucial for AI visibility. If you want to be cited in AI responses, you need to be present where AI systems learn. Here is what the evidence actually shows about LLM training data sources and how to optimize for each.
The Big Picture: How LLMs Are Trained
LLMs learn by processing enormous quantities of text from across the web and curated sources. The exact datasets vary by model and provider, and most do not publish their full training data composition. What researchers and public technical reports have documented:
Common Crawl is the dominant raw dataset used by most major LLMs. It is a nonprofit that crawls the public web and releases petabyte-scale snapshots. A 2023 survey of 47 LLMs for text generation found that at least 64% used some version of Common Crawl for pre-training.
Wikipedia is explicitly included in the training data of most major models because it provides structured, reliable factual content across millions of topics. GPT-3's original technical paper listed Wikipedia as one of its explicitly included sources.
Books and academic papers are standard inclusions, contributing depth on specialized topics.
Code repositories (primarily GitHub) are included by models designed to handle programming tasks.
Curated high-quality text is often added through processes like Reinforcement Learning from Human Feedback (RLHF) to improve response quality.
The "60-70% from web crawls" figure that appears in many articles is a rough generalization. The actual ratio varies substantially by model and is not publicly verified for most frontier models.
Platform Deep Dives: Optimization Strategies
Wikipedia: The Foundation of AI Knowledge
Why It Matters:
- Explicitly included in most major LLM training datasets
- Structured, cross-referenced factual content
- High trust signal due to editorial standards
- Available in multiple languages
Wikipedia's English edition surpassed 7 million articles in May 2025 and as of June 2026 contains over 7.1 million articles with more than 5 billion words. Its scale and editorial standards make it one of the most consistently cited sources across AI platforms.
Optimization Strategy:
-
Create Notable Brand Presence
- Meet Wikipedia notability guidelines (requires independent third-party coverage, set by Wikipedia's standards, not a marketing decision)
- Gather verifiable third-party coverage
- Build verifiable achievements
-
Contribute Valuable Information
- Edit relevant industry articles
- Add citations to your research
- Create missing topic pages
- Update outdated information
-
Build Wikipedia-Worthy Content
- Publish original research that gets cited elsewhere
- Create industry reports
- Generate newsworthy data
- Achieve industry milestones that receive independent coverage
Key Metrics:
- Articles mentioning your brand
- Citations to your content
- Wikidata connections
- Cross-language presence
Reddit: The Conversation Goldmine
Why It Matters:
- Real user opinions and experiences appear in many training corpora
- Problem-solving discussions train AI to understand real-world recommendations
- Product recommendations in authentic voice
- Note: Reddit's data licensing status for AI training changes over time. Reddit has entered licensing agreements with AI companies for training data access
Subreddit Prioritization:
-
Tier 1 (Highest Impact):
- r/technology
- r/programming
- r/entrepreneur
- r/marketing
- Industry-specific subreddits
-
Tier 2 (Strong Impact):
- r/AskReddit
- r/explainlikeimfive
- r/IAmA
- r/todayilearned
- Niche professional subreddits
Reddit Optimization Tactics:
## DO:
- Provide genuine value first
- Build karma organically
- Participate consistently
- Share unique insights
- Answer questions thoroughly
- Use data and examples
- Engage in discussions
## DON'T:
- Spam promotional content
- Use multiple fake accounts
- Buy upvotes
- Ignore subreddit rules
- Post low-effort content
- Be overly promotional
Content Strategy for Reddit:
-
Educational Posts
- Industry insights
- How-to guides
- Case studies
- Data analyses
-
Community Engagement
- Answer questions
- Share experiences
- Provide feedback
- Solve problems
-
AMA Sessions
- Expert knowledge sharing
- Brand awareness
- Thought leadership
- Direct engagement
Quora: The Q&A Authority
Why It Matters:
- Direct question-answer format is well-suited for training AI to answer questions
- High-quality, detailed responses demonstrate expertise
- Topic expertise demonstration
- Google search visibility drives additional discovery
Quora Optimization Framework:
Topic Selection:
- Industry-specific spaces
- Problem-solving topics
- Comparison questions
- How-to queries
- Best practices discussions
Answer Structure Template:
## Opening Hook
[Personal experience or surprising fact]
## Direct Answer
[Clear, concise response to question]
## Detailed Explanation
[In-depth information with examples]
## Supporting Evidence
- Statistics
- Case studies
- Research findings
- Expert quotes
## Practical Application
[Step-by-step guide or tips]
## Conclusion & CTA
[Summary and subtle brand mention]
Quora Success Metrics:
- Answer views
- Upvotes received
- Follower growth
- Space contributions
- Direct messages
Academic Papers & Research
Why It Matters:
- Highest authority signals for factual claims
- Peer-reviewed credibility
- Citation networks amplify reach
- Foundational knowledge that persists across training cycles
Publishing Strategy:
-
Research Papers
- Original studies
- Industry surveys
- Technical innovations
- Methodology papers
-
White Papers
- Industry analysis
- Best practices
- Framework development
- Solution comparisons
-
Case Studies
- Implementation details
- Results and metrics
- Lessons learned
- Reproducible methods
Distribution Channels:
- arXiv.org
- SSRN
- ResearchGate
- Academia.edu
- Industry journals
High-Authority News Sites
Target Publications:
- Tier 1: Forbes, WSJ, NYT, Guardian
- Tier 2: TechCrunch, Wired, VentureBeat
- Tier 3: Industry publications
- Tier 4: Regional news outlets
PR Strategy for LLM Visibility:
-
Newsworthy Angles
- Industry-first achievements
- Data-driven insights
- Trend predictions
- Substantive opinion pieces
-
Journalist Query Platforms
- Qwoted: connects journalists with verified experts
- Featured.com: acquired HARO in April 2025; runs expert roundups for Fortune, Fast Company, Yahoo
- Source of Sources: created by HARO's original founder
- Note: Connectively (the rebrand of HARO) shut down December 9, 2024
-
Press Release Distribution
- PRNewswire
- Business Wire
- PR Web
- Industry wires
The Stack Overflow Effect (For Tech)
Optimization Approach:
-
Answer Quality Questions
- Complex problems
- Common issues
- Best practices
- Tool comparisons
-
Create Canonical Answers
- Comprehensive solutions
- Code examples
- Performance comparisons
- Security considerations
-
Build Reputation
- Consistent participation
- High-quality answers
- Community moderation
- Tag expertise
GitHub: The Code Knowledge Base
Visibility Strategies:
-
Open Source Projects
- Popular libraries
- Useful tools
- Documentation
- Examples
-
README Optimization
- Clear descriptions
- Usage examples
- Installation guides
- API documentation
-
Community Building
- Issue responses
- Pull request reviews
- Discussion participation
- Star accumulation
Social Media's Influence on AI Training
LinkedIn (Professional Context)
- Thought leadership articles
- Industry discussions
- Company updates
- Professional achievements
- Generally included in crawled web data
X (formerly Twitter): Important Licensing Caveat
X updated its developer agreement in 2024 to prohibit third parties from using its data to fine-tune or train AI models. X also updated its own terms of service to allow itself to use user content for AI training. This means third-party AI providers face contractual restrictions on using X data, and the extent to which X data appears in current or future LLM training is subject to licensing agreements between X and individual AI companies.
YouTube (Video Knowledge)
- Tutorial content transcripts
- Expert interviews
- Product demonstrations
- Educational series
Optimizing for Future LLM Training
Emerging Patterns
-
Real-time Data Integration
- Models like Perplexity and ChatGPT with browsing access the live web
- Fresh content is accessible for retrieval-augmented generation (RAG) even outside training cycles
-
Multimodal Content
- Text + images
- Video transcripts
- Audio content
-
Structured Data Preference
- Schema markup
- JSON-LD
- Knowledge graphs
- API endpoints
Future-Proofing Strategies
-
Content Velocity
- Regular updates
- Fresh perspectives
- Timely responses
- Trend participation
-
Cross-Platform Presence
- Consistent messaging
- Platform-specific optimization
- Integrated campaigns
- Unified branding
-
Authority Building
- Expert positioning
- Citation accumulation
- Media mentions
- Industry recognition
Measurement Framework
Direct Metrics
- Wikipedia: Page views, citations, edits
- Reddit: Karma, mentions, discussions
- Quora: Views, upvotes, followers
- News: Articles, quotes, mentions
- Academic: Citations, downloads, references
Indirect Indicators
- AI response mentions (test manually)
- Brand recognition growth
- Organic traffic increases
- Expert status indicators
- Community engagement
Your 90-Day LLM Source Optimization Plan
Month 1: Foundation
Week 1-2:
- Audit current presence across platforms
- Identify gaps and opportunities
- Create platform accounts
- Develop content strategy
Week 3-4:
- Begin Reddit participation
- Start Quora contributions
- Submit first journalist query responses
- Plan Wikipedia strategy
Month 2: Acceleration
Week 5-6:
- Increase posting frequency
- Build platform authority
- Engage with communities
- Create cornerstone content
Week 7-8:
- Launch PR campaigns
- Publish research/data
- Expand platform presence
- Monitor AI citations
Month 3: Optimization
Week 9-10:
- Analyze performance data
- Refine strategies
- Scale successful tactics
- Build relationships
Week 11-12:
- Establish thought leadership
- Achieve platform milestones
- Document case studies
- Plan next quarter
Platform-Specific Best Practices
Reddit Best Practices
✅ DO:
- Read rules before posting
- Contribute 10x more than promote
- Use native Reddit formatting
- Engage authentically
- Provide proof when needed
- Respect community culture
- Build genuine relationships
❌ DON'T:
- Delete downvoted content
- Argue with moderators
- Use URL shorteners
- Cross-post excessively
- Ignore post timing
- Forget to follow up
- Be overly salesy
Quora Best Practices
✅ DO:
- Write comprehensive answers
- Include relevant images
- Cite credible sources
- Update old answers
- Follow topic spaces
- Build expertise slowly
- Engage with comments
❌ DON'T:
- Copy-paste content
- Over-promote products
- Write short answers
- Ignore question intent
- Use clickbait tactics
- Neglect formatting
- Spam multiple answers
Common Mistakes to Avoid
-
Over-Optimization
- Appearing inauthentic
- Gaming metrics
- Ignoring community values
-
Platform Neglect
- Inconsistent presence
- Abandoned profiles
- Outdated information
-
Quality Compromise
- Prioritizing quantity
- Generic content
- Poor research
-
Measurement Gaps
- No tracking system
- Ignoring feedback
- Missing opportunities
Tools & Resources
Monitoring Tools
- Google Alerts - Brand mentions
- Mention - Social listening
- Ahrefs - Backlink tracking
- SimilarWeb - Traffic analysis
Content Tools
- Grammarly - Writing quality
- Hemingway - Readability
- Canva - Visual content
- Loom - Video creation
Analytics Platforms
- Reddit Analytics - Subreddit stats
- Quora Stats - Answer performance
- Google Analytics - Traffic sources
- Genmark AI GEO - AI visibility tracking
The Genmark AI Advantage
Our platform helps you optimize for LLM sources:
- Multi-platform tracking across all sources
- AI citation monitoring in real time
- Competitive intelligence on rival strategies
- Optimization recommendations based on data
- ROI measurement for all efforts
Key Takeaways
- Wikipedia and Reddit are among the most influential training sources for most LLMs
- Authority and authenticity matter more than volume
- Platform-native optimization yields best results
- Consistency and quality build long-term visibility
- Cross-platform presence maximizes AI citations
- Social media licensing has changed — X/Twitter data access for third-party AI training is now contractually restricted
Next Steps
- Download our Platform Optimization Checklist →
- Read our Reddit & Quora Strategy Guide →
- Start your free Genmark AI trial →
Sources
- Common Crawl Wikipedia (scale and role in AI training)
- Mozilla Foundation: Common Crawl's Impact on Generative AI (training data analysis)
- Wikipedia:Size of Wikipedia (article count and scope; as of June 2026: 7.1M+ English articles)
- Otterly.ai: LLM Knowledge Cutoff Dates (2026 Updated) (current model cutoffs)
- TechCrunch: ChatGPT reaches 900M weekly active users (February 2026)
- Perplexity AI Statistics 2026 (DemandSage) (query volumes)
- X developer terms restricting AI training use (2024)
- Social Media Today: X updates Terms of Service for AI training
- Featured.com (current HARO operator) (journalist query platform)
- Prezly: HARO Alternatives 2026
Related Resources
Last updated: June 22, 2026 | Part of Genmark AI's AI Visibility Learning Center
See where AI leaves your brand out
Put this into practice. Genmark AI shows you exactly how ChatGPT, Gemini, Perplexity and the other major engines answer about your brand — then helps you create the content that earns the citation.
Complimentary AI visibility report · No account needed