Two Ways LLMs Access Content
Large Language Models use your content in two completely different ways. Understanding this difference is crucial for optimizing your content strategy. One happened in the past and you cannot control it now. The other happens every time someone asks a question, and this is where you have the most opportunity.
The first way is through training data. Your content might have been included when the LLM learned how to understand language. The second way is through retrieval, where the LLM actively searches for and cites your content when answering questions.
Modern AI search engines like ChatGPT with browsing, Perplexity, and Claude with tools rely heavily on retrieval. This means your current content can be discovered and cited today, not just used in past training. This is the biggest opportunity for content creators right now.
Training Data: The Past
When companies build a new LLM, they collect massive datasets from the internet. Your content might have been part of this collection if it was publicly available. The LLM learned patterns from your content along with billions of other sources.
How Training Works
Companies scrape publicly available content from the web. This includes websites, forums, books, articles, and more. The content is processed and used to teach the model how language works.
The model does not memorize your exact words. Instead, it learns statistical patterns about language. It learns grammar, facts, reasoning, and how different concepts relate to each other.
Training happens once for each model version. After training is complete, the model has a knowledge cutoff date. It cannot learn from new content published after this date unless it uses retrieval.
Important Facts About Training Data
You Cannot Get Attribution
When an LLM uses knowledge from its training, it cannot cite the original source. The model learned patterns from millions of sources. It cannot track which specific content contributed to each piece of knowledge. This is like a student who read many books but cannot remember which book taught them each fact.
Training Data Is Historical
Most LLMs have a knowledge cutoff date in their past. For example, GPT-4 was trained on data up to September 2021 initially. Claude 3 has more recent training data, but it still has a cutoff. Content published after the cutoff date only matters for retrieval, not training.
You Cannot Control Past Training
If your content was publicly available during a training period, it might have been included. You cannot remove it from the model after training is complete. However, you can control whether future models can access your content by using robots.txt or AI-specific access controls.
While you cannot control past training, you have full control over retrieval. This is where your optimization efforts should focus today.
Retrieval: The Present (Your Biggest Opportunity)
Retrieval is when an AI search engine actively searches for your content to answer a question. This happens in real-time, every time someone asks a question. When your content is retrieved and used, you get citation and attribution.
How Retrieval Works
When someone asks ChatGPT (with browsing), Perplexity, or similar AI a question, the system searches the web for relevant content. It looks for pages that match the query and meet quality standards. The best matches are retrieved, read, and used to generate the answer.
The AI cites sources it used. This gives you credit and can drive traffic to your site. This is similar to how traditional search works, but the AI reads your content and synthesizes it into an answer.
This process is called Retrieval Augmented Generation (RAG). Learn more in How AI Search Engines Work.
Why Retrieval Matters More Today
You Get Attribution
When AI retrieves your content, it cites you as a source. Your brand gets visibility and credibility. Users can click through to read more on your site.
It Happens in Real-Time
New content you publish today can be retrieved and cited immediately. You do not need to wait for a new model to be trained. This makes fresh content valuable.
You Can Optimize for It
You have control over how your content is structured and presented. Better optimization means higher chances of being retrieved. Your GEO-Score measures this.
It Is Measurable
You can track when your content gets cited. Some AI platforms show which sources they used. This helps you understand what works and improve over time.
The Timeline: When Content Gets Used
Understanding the timeline helps you see when your content can be accessed by AI systems. This visualization shows how content moves from creation to AI usage.
You Publish Content (Today)
You publish a blog post, article, or webpage. The content is now live and accessible on the internet. It is publicly available for both humans and bots to read.
Impact: Immediate eligibility for retrieval by AI search engines.
AI Bots Discover It (Hours to Days)
Search engine crawlers and AI bots visit your site. They index your content and add it to their databases. This makes your content searchable by AI retrieval systems.
Impact: Your content is now in the retrieval pool for AI search engines. Make sure you allow AI bots to access your content.
Retrieval Usage (Ongoing)
When someone asks a relevant question, AI search engines may retrieve your content. They read it, extract information, and cite it in their answers. This can happen repeatedly as long as your content remains relevant and high-quality.
Impact: You get citations, traffic, and brand visibility. This is your main opportunity for AI exposure today.
Possible Training Inclusion (Months to Years Later)
When companies train new LLM versions, they collect recent web data. Your content might be included in this training dataset. The LLM learns patterns from your content but does not cite you for this learned knowledge.
Impact: Your content influences the model's general knowledge, but you get no attribution. This is less important than retrieval for most creators.
What Makes Content Get Selected for Retrieval?
Not all content has an equal chance of being retrieved and cited. AI systems use sophisticated ranking to choose the best sources. Understanding these selection criteria helps you optimize effectively.
Top Selection Factors
Relevance to Query
Your content must match what the user is asking about. AI looks for semantic relevance, not just keyword matching. Content that directly answers the question ranks higher.
Content Quality
High-quality, well-researched content gets priority. AI evaluates depth, accuracy, and comprehensiveness. Thin or low-value content rarely gets retrieved.
Authority and Trustworthiness
Content from authoritative sources ranks better. AI looks at domain authority, backlinks, and citations. Established sites have an advantage over new ones.
Freshness
For time-sensitive topics, recent content ranks higher.Regular updates signal that your content is current and accurate. Outdated content gets deprioritized.
Readability and Structure
Clear, well-structured content is easier for AI to parse and use. Good readability and structure improve your chances. Complex or poorly organized content gets skipped.
Accessibility
Your content must be crawlable by AI bots. Paywalls, login requirements, or robot restrictions limit retrieval. Publicly accessible content has the best chance.
Citation Behavior: How AI Credits Sources
Different AI systems handle citations differently. Understanding this helps you know what to expect and where to focus your efforts.
Systems That Cite Sources
- •Perplexity AI: Always cites sources with numbered footnotes and links
- •ChatGPT (with browsing): Cites sources when it searches the web
- •Bing Chat: Provides source links in responses
- •Google Gemini (with search): Shows sources for web-based answers
Systems That Usually Do Not Cite
- •ChatGPT (base): Answers from training data without citations
- •Claude (without tools): Uses training data, no source attribution
- •Most open-source models: Generate from learned patterns only
The trend is moving toward more citation and attribution. AI companies recognize the importance of crediting sources and building trust. Optimize for retrieval systems that cite sources to maximize your visibility.
How to Increase Your Chances of Being Cited
Focus your optimization efforts on retrieval, not training. This is where you can have the most impact today.
- •Create comprehensive, high-quality content that fully answers questions
- •Use clear content structure with descriptive headings
- •Maintain good readability so AI can easily understand your content
- •Keep content fresh and updated to stay relevant
- •Ensure AI bots can access your content
- •Build authority through quality citations and backlinks
- •Monitor your GEO-Score to track optimization progress