LLM Training Data Analysis: What are LLMs trained on and how can we use that for our SEO / GEO strategy?

What Are Large Language Models (LLMs) and How Are They Trained?

LLMs are pre-trained models based on Transformer architectures and are designed to analyze and generate text by learning complex data patterns across billions of data points. Their training process comprises several phases:

The training process in detail

1. Data Collection: Aggregation of diverse, high-quality data sets from websites, literary sources, user content from publicly available open corpora (such as Common Crawl or Wikipedia).
2. Data Processing: Data cleansing and tokenization, which includes the conversion of raw text into usable inputs for training.
3. Model Training: Using deep learning techniques to optimize parameters; models are trained to predict the next word/token to improve generative textual and contextual understanding.
4. Fine Tuning: Fine tuning based on domain-specific or task-specific data sources to make LLMs ready for specific Natural Language Processing (NLP) tasks such as sentiment analysis, summarization or machine translation. ¹

Static vs. dynamic training

Static training: Until they are retrained, LLMs rely on static information pre-trained on large datasets from many sources, including books, websites and public corpora.

Live research capabilities: Some contemporary LLM systems have real-time online search capabilities that allow them to retrieve and integrate up-to-date information while interacting with users via computing resources.

Implementation example: Perplexity AI and other AI systems use live research capabilities in conjunction with pre-trained models to provide up-to-date, relevant answers that go beyond their original training data. ^{2 3 4}

The role of human feedback

Experts emphasize the value of thorough pre-training on large, diverse data sets to build a strong foundation for language understanding. They emphasize that fine-tuning – including reinforcement learning with human feedback (RLHF) – is essential to improve response quality and align model outputs with human values.

Effective learning requires high-quality data pre-processing, including cleansing and tokenization. In addition, incorporating live research or retrieval augmented generation improves the ability of models to provide up-to-date and relevant knowledge, although static training remains the foundation. Human feedback is recognized as an excellent practice to reduce bias and improve the safety and usefulness of LLMs at every stage. ^{5 6 7}

What Types of Data Are LLMs Trained On?

Training mainly uses large-scale textual data sources:

• Web Pages: Articles, forums, blogs, and encyclopedias constitute the bulk of the data, providing diverse contexts and language styles.
• Books and Literature: Structured high-quality text offers depth and language formality.
• Open Datasets: Collections like Common Crawl and Wikipedia ensure varied, multilingual text data.
• User-Generated Content: Forums and reviews give conversational examples and express varied sentiments.
• Programming Code: Some LLMs include code repositories to support generation and understanding of different programming languages. ^{8 9 10}

Overview of training data types and SEO recommendations

Training Data Type	Description	Impact on LLM Outputs	SEO / GEO Strategy Recommendation
Web Pages	Diverse internet content: blogs, news, forums	Broad topic coverage, conversational language understanding	Create comprehensive, topic-rich pages with natural language and FAQs
Books and Literature	Structured, formal textual content across domains	High-quality, authoritative language and concepts	Develop in-depth authoritative articles with citations and expert tone
Open Datasets (Wikipedia, Common Crawl)	Curated, multilingual, and comprehensive corpora	Balanced knowledge base, multilingual capabilities	Use clear entity mentions, multilingual content, and structured data
User-Generated Content	Forums, reviews, comments expressing varied sentiment	Understanding of real user language, sentiment, and intent	Incorporate user questions, reviews, and conversational content formats
Programming Code Repositories	Source code and technical documentation	Support for code generation and programming language tasks	Provide technical FAQs, code snippets, and documentation optimized for developers
Structured Data	Embedded metadata providing context to unstructured text	Easier entity recognition and precise content parsing	Implement schema.org markup (FAQ, Product, Article) for AI readability
Synthetic Data	AI-generated or augmented text to supplement training	Expands diversity and coverage, fills data gaps	Use generated summaries or FAQs to complement human-written content, ensuring accuracy

How Does Training Data Influence LLM Outputs and SEO Visibility?

Understanding these influences is crucial for your SEO strategy:

• Data diversity & coverage: Comprehensive data on topics enables reliable and coherent text generation.
• High-quality & trustworthy data sources: LLMs implicitly rank according to learned authority; well-cited, structured and factual content is preferred.
• Recency limits: Without retraining, models are limited to static data. Hybrid approaches such as Retrieval Augmented Generation (RAG) integrate live data.
• Entity-centric understanding: LLMs focus on entities (people, places, brands) and their relationships in order to build contextual knowledge beyond keywords. ^{11 12 13}

Therefore, your SEO tactics need to evolve from pure keyword stuffing to rich, authoritative and structured content strategies that are optimized for language models.

Practical Strategies to Use LLM Training Data Insights for SEO / GEO

1. produce authoritative content

Focus on expertise, clear facts and cite credible sources to match the way LLMs evaluate trusted inputs.

The numbers speak for themselves:

• A study by Seer Interactive shows a 65% correlation between Google Page 1 rankings and mentions in AI searches. ¹⁴
• According to 72% of marketers, the best SEO strategy for 2025 will be producing high-quality, authoritative content that also generates 77% more backlinks, increasing visibility, optimal performance and authority. ¹⁵

2. answer specific user questions

Use FAQs and conversational content that mimic natural query formulations that drive LLM responses.

3. implement schema markup

Structured data such as FAQ, article and organization schemas support LLMs in entity recognition.

The benefit: Rich snippets are 40% more likely to display pages with schema markup, increasing the likelihood of AI-driven search functions. ¹⁶

4. update content regularly

To overcome the static training limitations of LLMs, keep your information fresh for RAG-enabled platforms (Retrieval-Augmented Generation).

The result: AI-powered search engines that combine real-time data can increase clicks by up to 38%. ¹⁷

5. improve entity clarity

Explicitly mention brands, locations and product names along with contextual relationships. ^{18 19}

6. use platforms with RAG models (Retrieval-Augmented Generation)

RAG systems: is an architectural approach for AI models (usually LLMs) in which two components are combined:

1. Retrieval (retrieval): The model accesses an external knowledge source (e.g. vector database, search index, internal documents, web pages) in response to a query.
2. Augmented generation (extended response): The retrieved, relevant texts are added to the prompt. The LLM uses this information to generate a response.

Work with services that combine LLMs and real-time data for higher visibility.

The impact: Organic impressions and engagement are significantly increased when LLMs and real-time retrieval are combined. ²⁰

7. high-quality backlinks and citations

Trustworthy references increase the implicit authority of your content in training corpora.

The facts: Active blogs have 97% more backlinks and top ranking pages have 3.8 times more, reinforcing the content authority shown in LLM training data. ^{21 22}

Examples of Training Data Impact on GEO

Brand Visibility and Citation Influence

The figures clearly show how important authority is for your AI visibility:

• A 2025 study found that 250,000 citations had been used out of 40,000 search queries. This means that high-quality citations could increase the likelihood of a mention.
• AI models prioritize content from trusted sources such as third-party editorials and user reviews in tabular data form.
• This citation frequency often correlates with actual market share and brand awareness.
• Therefore, publishing authoritative and widely cited content significantly increases your brand visibility in AI outputs. ^{23 24}

Platform-Specific UGC Preferences (User Generated Content)

Each AI search engine shows preferences for different UGC sources. You should take this into account in your content strategy:

• Perplexity: Favors YouTube and PeerSpot
• Google Gemini: Frequently cites Medium, Reddit, and YouTube
• ChatGPT: Often references LinkedIn, G2, and Gartner Peer Reviews

Conclusion: Use LLM training data knowledge for your business growth

Understanding the nature and types of LLM training data sets, coupled with data processing and model behavior, empowers you as a marketer to optimize content for future-proof SEO and GEO success.

By prioritizing high-quality data sets, leveraging structured data and focusing on entities and contexts rather than pure keywords, you can secure your company’s presence in the AI-dominated content ecosystem.

Your next steps:

1. Audit your existing content for authority and structure
2. Implement schema markup for better AI recognizability
3. Develop FAQ sections that answer natural user queries Systematically
4. Build high-quality backlinks
5. Keep your content regularly updated for RAG systems

References:

1. Ju, Yiming, and Huanhuan Ma. „Training Data for Large Language Model.“ arXiv preprint arXiv:2411.07715, 12 Nov. 2024. Summary of pretraining and fine-tuning data practices, data scale, and collection methods for state-of-the-art LLMs. URL: https://arxiv.org/abs/2411.07715  ↩︎
2. Research AIMultiple. Large Language Model Training in 2025. Describes how LLMs are typically pretrained on large, static datasets from diverse internet and public sources and can only be updated via retraining or fine-tuning.
   URL: https://research.aimultiple.com/large-language-model-training/  ↩︎
3. Shakudo. Top 9 Large Language Models as of July 2025. Reviews modern LLM platforms, including the integration of real-time search capabilities for live research, and highlights Perplexity AI as a leading example of LLMs that combine pretrained knowledge with live web access.
   URL: https://www.shakudo.io/blog/top-9-large-language-models ↩︎
4. Rohan Paul. Selecting and Preparing Training Data for LLMs (2024–2025). Discusses static dataset reliance for model pretraining and contrasts it with emerging architectures incorporating retrieval-augmented or live-research features. URL: https://www.rohan-paul.com/p/selecting-and-preparing-training ↩︎
5. Research AIMultiple. Large Language Model Training in 2025. Summary of data collection, preprocessing, training, and fine-tuning processes, highlighting the importance of diverse and high-quality sources like Common Crawl and Wikipedia.
   URL: https://research.aimultiple.com/large-language-model-training/ ↩︎
6. Rohan Paul. Selecting and Preparing Training Data for LLMs (2024–2025). Covers best practices for ensuring diverse, high-quality datasets, including cleaning, tokenization, and multi-source data integration for robust LLM performance.
   URL: https://www.rohan-paul.com/p/selecting-and-preparing-training  ↩︎
7. ScrapingAnt. Open Source Datasets for Machine Learning and Large Language Models. Explores key characteristics of high-quality datasets, ethical considerations, and examples such as RedPajama used for LLM development.
   URL: https://scrapingant.com/blog/open-source-datasets ↩︎
8. Wang, Zhou, et al. „Leveraging Open-Source Large Language Models for Data Augmentation in Text Classification.“ PubMed Central (PMC), 19 Nov. 2024. Details on LLaMA model training on publicly available datasets focusing on transparency and performance.
   URL: https://pmc.ncbi.nlm.nih.gov/articles/PMC11590755/ ↩︎
9. Kaubrė, Vytenis. „LLM Training Data: The 8 Main Public Data Sources.“ Oxylabs Blog, 27 Sept. 2024. Overview of major public data sources used for LLM training such as Common Crawl, Wikipedia, GitHub, and scientific repositories.
   URL: https://oxylabs.io/blog/llm-training-data ↩︎
10. Peng, Ke, et al. „A Comprehensive Overview of Large Language Models.“ arXiv preprint arXiv:2307.06435, July 2023. Provides technical insights on dataset types, training methodologies, and multilingual considerations for LLMs.
    PDF: https://arxiv.org/pdf/2307.06435.pdf ↩︎
11. Smith, John, et al. „A Comprehensive Review of Large Language Models: Issues and Applications.“ Sustainable Computing: Informatics and Systems, vol. 40, 14 Jan. 2025, Springer. Review addressing LLM training challenges and their practical uses in various domains.)
    DOI: https://doi.org/10.1007/s43621-025-00815-8 ↩︎
12. Lee, Han, et al. „Future Applications of Generative Large Language Models: A Data-Driven Survey.“ Neurocomputing, vol. 530, Feb. 2025. Explores evolving use cases and data-driven analysis of LLM tasks and user intent understanding.
    URL: https://www.sciencedirect.com/science/article/pii/S016649722400052X ↩︎
13. Chen, Mei, et al. „Industrial Applications of Large Language Models.“ Scientific Reports, vol. 15, no. 1, 21 Apr. 2025. Explanation of large-scale training data used for LLMs and impacts on complex NLP tasks.
    URL: https://www.nature.com/articles/s41598-025-98483-1  ↩︎
14. Research Seer Interactive. What is Generative Engine Optimization (GEO) & how does it impact SEO? Explains how GEO differs from traditional SEO, outlines the types of generative AI search systems (training-based, hybrid, conversational), and why modern SEO fundamentals remain essential for visibility in AI-driven environments.
    URL: https://www.seerinteractive.com/insights/what-is-generative-engine-optimization-geo ↩︎
15. Question-based titles CTR and long-form content backlink benefits:
    SEO Sherpa, „70+ SEO Statistics for 2025 (That Actually Matter),“ July 2025
    URL: https://seosherpa.com/seo-statistics/  ↩︎
16. Schema markup benefits and AI-driven search freshness boost:
    SEO.ai and Influencer Marketing Hub industry reports and AI SEO statistics insights from 2025
    URL: https://www.seo.com/ai/ai-seo-statistics/ ↩︎
17. Schema markup benefits and AI-driven search freshness boost:
    SEO.ai and Influencer Marketing Hub industry reports and AI SEO statistics insights from 2025
    URL: https://www.seo.com/ai/ai-seo-statistics/  ↩︎
18. Kaubrė, Vytenis. „LLM Training Data: The 8 Main Public Data Sources.“ Oxylabs Blog, 27 Sept. 2024. Overview of major public data sources used for LLM training such as Common Crawl, Wikipedia, GitHub, and scientific repositories.
    URL: https://oxylabs.io/blog/llm-training-data ↩︎
19. Wang, Zhou, et al. „Leveraging Open-Source Large Language Models for Data Augmentation in Text Classification.“ PubMed Central (PMC), 19 Nov. 2024. Details on LLaMA model training on publicly available datasets focusing on transparency and performance.
    URL: https://pmc.ncbi.nlm.nih.gov/articles/PMC11590755/ ↩︎
20. Research HubSpot. 2025 Marketing Statistics, Trends & Data. Provides key data points like 59 % of Americans find most marketing emails useless; 40 % of email users have at least 50 unread messages; 41 % of email views come from mobile devices; 70 % of marketers rate their leads as “high quality”; and breakdowns of generational targeting in 2024 (e.g., 36 % target Gen Z, 72 % Millennials).
    URL: https://www.hubspot.com/marketing-statistics  ↩︎
21. Question-based titles CTR and long-form content backlink benefits:
    SEO Sherpa, „70+ SEO Statistics for 2025 (That Actually Matter),“ July 2025
    URL: https://seosherpa.com/seo-statistics/  ↩︎
22. Research HubSpot. 2025 Marketing Statistics, Trends & Data. Provides key data points like 59 % of Americans find most marketing emails useless; 40 % of email users have at least 50 unread messages; 41 % of email views come from mobile devices; 70 % of marketers rate their leads as “high quality”; and breakdowns of generational targeting in 2024 (e.g., 36 % target Gen Z, 72 % Millennials).
    URL: https://www.hubspot.com/marketing-statistics ↩︎
23. Search Engine Journal. „How to Get Cited by AI: SEO Insights from 8,000 AI Citations.“ Analysis of brand visibility in AI-generated outputs and the impact of citation frequency on AI rankings.
    URL: https://www.searchenginejournal.com/ai-search-engines-often-cite-third-party-content-study-finds/540692/  ↩︎
24. Digital Silk. „AI Statistics In 2025: Key Trends And Usage Data.“ Market research report covering AI trends in various industries including software sector adoption and brand influence on AI models.
    URL: https://www.digitalsilk.com/digital-trends/ai-statistics/  ↩︎