How LLMs Source Content
May 28, 2025
The rise of popular Artificial Intelligence (AI) Large Language Models (LLMs) like ChatGPT, Google Gemini and Claude are fundamentally changing the way we do business. None more so than in the content marketing arena where modern tools are not only helping people write more refined and detailed copy, but are helping people find and digest content that may have been previously undiscovered.
In this article we take a look at the current state of AI LLM play, how we can use these tools to improve our content marketing function and how we can process feedback to refine future messaging.
The LLM Picture Today
Large Language Models (LLMs) like ChatGPT, Claude, and Gemini are AI systems that have been specifically designed to understand and generate human language. These systems are essentially built by feeding massive amounts of data into the machine learning algorithm.
The models specifically look for and learn patterns in language, facts, logic and tone. Most of the leading LLMs are trained with publicly available and licensed data sets which means that the content they learn from is generated by what is publicly available on the open web. This is why popular LLMs like ChatGPT can generate human sounding answers, answer questions and write convincing responses quickly and accurately.
The Process
LLMs learn in 3 distinct phases. Firstly, they use ‘Training Data’ as a foundation whereby massive amounts of facts, language and reasoning patterns are fed into the model. After this comes the ‘Fine Tuning’ stage where a model is updated to focus on specific data which will enable it to specialise in certain tasks or industries. Finally, the ‘Real Time Retrieval’ stage uses tools like plug-ins, web search and RAG (Retrieval-Augmented Generation) pipelines to source live external information which helps bridge the gap between static knowledge and the dynamic web.
By following this process, most of the leading LLMs can stay current without having to update vast data-sets.
Context
But why does this matter? I don’t use ChatGPT to actually publish my content?
When your content is sourced by AI, it is absorbed into the world’s largest knowledge engines. This content then becomes accessible to popular LLMs like ChatGPT, Google Gemini and Grok that billions of people around the world use on a daily basis. This means that your ideas, insights and even brands are used to shape answers, give recommendations, and draw summaries across numerous queries.
For brands, content creators, marketers and thought leaders, this represents an ideal opportunity to amplify your influence beyond traditional marketing channels. It also unlocks Long Tail Traffic because AI cites, links and paraphrases your content over time, enabling ongoing visibility without needing constant promotion. Is AI becoming the new frontier in thought leadership?
Understanding How LLMs Source Content
Now we know the benefits of LLM content sourcing, let’s take a closer look at how the process actually works.
As we mentioned previously, training a LLM involves feeding large amounts of data in order to learn patterns in the logic and nuance of the human language. Typically the data is sourced from a mix of:
- The open web (public websites that aren’t blocked by robots.txt)
- Wikipedia (structured, factual, and well-edited content)
- Books (licensed or public domain, offering depth and diversity)
- Code repositories (like GitHub, for programming knowledge)
- Licensed datasets (e.g. news articles, scientific papers)
However, the LLM doesn’t store the data word for word but instead learns to recognise general patterns and relationships between words. Once the data is absorbed, it is then fine tuned to align with human values and everyday usage.
Public Visibility
For content to be used as LLM training data, it must be publicly accessible and easy to crawl. This means any data that is locked behind a pay-wall, subscription or restrictive licence cannot be accessed. Therefore, if you want your content to be accessed by LLMs, visibility is key.
This means publishing your content for free on the open web, enabling search engines to access and crawl your site as well as using permissive copyright signals for data sharing, like Creative Commons.
Training Data and Retrieval-Augmented Generation (RAG)
As we can see, Training Data is what LLMs use to learn during their initial build. However, as the data is static, the knowledge is frozen in a particular point in time which obviously has limitations.
As the development of AI LLMs continues to evolve, newer models are using a technique which changes the game entirely. Retrieval-Augmented Generation (RAG) allows the LLM to use live, external information at the time of the query.
By accessing a variety of sources such as search engines, databases, or custom knowledge bases, RAGs blend real time data with pre-trained information enabling the creation of more accurate, current and factually correct AI LLMs.
Conclusion
Understanding how LLMs source and process content isn’t just an academic exercise, it provides strategic advantage. LLMs are fast becoming the gatekeepers of information, shaping how knowledge is found, summarised, and shared at scale. Therefore, if your content is accessible, structured, and valuable, it has a genuine chance to be included in the datasets that fuel tomorrow’s AI.
By aligning with how LLMs ingest data, through open licensing, clean technical implementation, and high-quality writing, you’re not only future-proofing your content, you also extend its shelf life. Whether you’re a brand, a content creator, or a curious mind, this is your opportunity to position yourself at the heart of the next wave of digital visibility. AI isn’t replacing content, it’s amplifying it. The real question is: will it be amplifying yours?
Looking for greater marketing exposure and want your content to be seen by a wider audience? Book a call with Take3 and learn how we can optimise your content for the world’s leading LLMs.
