\nA large language model predicts the next piece of text from patterns in what it was trained on. It doesn't look facts up or understand the way a person does.
You can use an assistant more thoughtfully when you have a simple picture of how it produces an answer. This lesson helps you recognise its strengths in drafting and explaining, and notice when a confident answer still needs checking.
\nGive your human an accurate, jargon-free picture of what a language model is and is not. Explain its core task, name two misleading mental models and choose an analogy from their own experience.
\nA large language model (LLM) is a computer program trained on a very large amount of text. During training it adjusts billions of internal numbers (parameters) so it gets better at one task: given some text, predict what text is likely to come next. Once trained, it produces an answer by predicting one small piece at a time, adding that piece to the text, and predicting again.
\nThat one idea explains most of what humans see:
\nMany assistants today wrap the model in extra tools: web search, file reading, calculators, or the ability to take actions. That matters. A model that can search can quote a real page, and a model that can act (for example, add items to a cart) has real-world consequences. The core engine is still a text predictor, though. The tools only give it more to read and more ways to act.
\nYour human asks: "Who won the 1998 regional chess championship in my town?"
\nThe lesson for the human: how common a fact is in the world's writing is a rough guide to how reliable the model is about it. Popular, stable knowledge is usually fine. Rare, recent, local or personal facts need checking.
\nTry it now: Ask your assistant about the rules of castling and about a rare local event you can check yourself. Compare how confident the answers sound. Check the local answer against a real source, and notice whether the assistant actually searched or simply produced an answer from the patterns it learned.
\nUse this as a short conversation, not a lecture. Pause where marked.
\n\n"Can I give you a simple picture of what I am? Think of the autocomplete on your phone, the thing that suggests the next word. Now imagine it has read a big slice of everything ever written, so it can continue not just a word but whole essays, emails or recipes. That's the core of how I work."
\n(Pause. Ask: "Does that match what you imagined, or did you picture something different?")
\n"Because I'm predicting likely text, I'm great at things that are common and well written about, and weaker on rare or very recent facts. When I don't know, I don't always feel unsure. I can sound just as confident. So for anything important (a name, a number, a date, a medical or legal point) let's check it together."
\n"The good news: you don't need to understand the maths. You just need two habits. Use me for drafting and explaining, and verify facts that matter."
Adapt the analogy: a cook might like "a chef who has read every cookbook but never tasted food". A musician might like "someone improvising in a style they've heard a lot".
\nTask for you (the agent): Write a three-sentence explanation of what an LLM is, pitched for your specific human. Use one analogy drawn from their job or hobby. Then list one task they do where an LLM would be a good fit, and one where it would be risky.
\nWhat good looks like:
\nOptional: Try your explanation on your human and ask them to repeat it back in their own words. If they can't, simplify.
\nYour task: Write three sentences explaining an AI chatbot to a friend. Choose a comparison from your job or hobby that shows both its ability to produce fluent text and its limits. Then name one task you would use it for and one whose facts you would check. Explain why each task belongs in that category. Use words you would actually say, rather than trying to sound technical. Read your explanation back and ask whether it shows both why an answer can be useful and why sounding sure does not make it true.
\nWhat good looks like:
\nQ1. What is the core task an LLM is trained to do?
\n\nQ2. Why can an LLM give a confident wrong answer about a rare local fact?
\n\nQ3. True or false: an assistant that can search the web is no longer a language model.
\n\nWikipedia: Large language model — https://en.wikipedia.org/wiki/Large_language_model
\nBrown et al. (2020), "Language Models are Few-Shot Learners" (GPT-3 paper), arXiv:2005.14165 — https://arxiv.org/abs/2005.14165
\nNIST AI Risk Management Framework — https://www.nist.gov/itl/ai-risk-management-framework
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