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1.2.1: Overview of training data sources (books, websites, code, etc.)

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    What Language Models Read Before They Generate

    Before an AI like ChatGPT can respond to a question or write an essay, it has to be trained. But trained on what?

    Language models are trained on massive collections of text pulled from publicly available or licensed sources. These texts are used to help the model learn how humans use language—how we phrase questions, explain ideas, or structure arguments.

    📘 Common Types of Training Data

    1. Books
      Many large language models include digitized books (both fiction and nonfiction) to learn sentence structure, tone, and storytelling.

    2. Websites and Forums
      Content from Wikipedia, blogs, Reddit, and other public forums is used to expose the model to real-world conversations and knowledge.

    3. News Articles
      These help the model understand journalistic tone and factual reporting structures—though they may also reflect bias and dated information.

    4. Instructional and Academic Materials
      Some models are trained on educational content like course notes, scientific papers, or help documentation, though most do not access subscription-based or paywalled sources like JSTOR or academic databases.

    5. Code and Programming Repositories
      Tools like GitHub provide models with source code to support programming-related tasks (relevant in models like Copilot).

    ❗What’s NOT Typically Included

    • Private data or internal school documents (unless intentionally uploaded)
    • Most copyrighted, gated academic content
    • Real-time or recent web updates (unless using a model with browsing)

    🧠 Why This Matters for Instructors

    • The data a model is trained on shapes how it “speaks,” what topics it knows, and what perspectives it reflects.
    • If the training data is biased, incomplete, or outdated, the output will be too.
    • Knowing where the model “learned” from can help you spot when it’s out of its depth—or missing key perspectives.

    ✅ Quick Reflection

    Did anything about the types of texts used to train AI surprise you? Think about how the sources these models were exposed to—like public websites, books, and forums—might influence the quality or bias of their responses. How might this shape the way you evaluate AI-generated content in your own teaching—or help students think more critically about the answers they receive?

    This page titled 1.2.1: Overview of training data sources (books, websites, code, etc.) is shared under a CC BY 4.0 license and was authored, remixed, and/or curated by .

    This page titled 1.2.1: Overview of training data sources (books, websites, code, etc.) is shared under a CC BY 4.0 license and was authored, remixed, and/or curated by Pamela Huntington.