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  "description": "I was talking to my mother-in-law this week about how she downloaded ChatGPT to her phone two weeks ago. It’s her first experience with an LLM. We talked about all the ways she’s started using it, but also the common things that people misunderstand about how they work. It got me thinking that this would be worth writing about.\n\nSend this article to someone who is just starting out with ChatGPT, Claude, or Gemini. I hope it’s helpful.\n\n—\n\nIf you’ve been sent this article, it should mostly explai",
  "path": "/some-common-misconceptions-about-llms/",
  "publishedAt": "2026-06-22T21:14:31.000Z",
  "site": "https://www.aaronmiller.info",
  "tags": [
    "growing rather than building",
    "being-nice part",
    "50 First Dates",
    "It doesn’t",
    "my favorite AI YouTube channels"
  ],
  "textContent": "_I was talking to my mother-in-law this week about how she downloaded ChatGPT to her phone two weeks ago. It’s her first experience with an LLM. We talked about all the ways she’s started using it, but also the common things that people misunderstand about how they work. It got me thinking that this would be worth writing about._\n\n_Send this article to someone who is just starting out with ChatGPT, Claude, or Gemini. I hope it’s helpful._\n\n—\n\nIf you’ve been sent this article, it should mostly explain why AI has done stupid or confusing things. It will also help you think better about what AI can do reliably. It’s not a deep-dive, but just covers a handful of common misconceptions.\n\nQuick note: when I say LLM, it’s short for Large Language Model, which is what Claude, ChatGPT, and Gemini all are. I’ll alternate between “LLM”, “model”, and “AI”, but I mean the same thing.\n\n## AIs are much more human-like and much less computer-like than most people think.\n\nIt’s natural when you’re using a computer program to assume it has all the predictability and precision that computers have. When you type on your keyboard, the letters you press appear on the screen. Unless something has broken, that happens just as expected every time.\n\nAIs are a lot less computer-like than this. It’s not their fault; it’s primarily because they operate on human language. LLMs are created by having a very large computer program learn patterns in all different kinds of human language, including everything from French, English, and Chinese to computer code and math equations. They get very good, but not perfect, at predicting what to say next based on those patterns.\n\nLanguage makes AIs surprisingly similar to people, because language contains a bunch of our tendencies, perspectives, and ways of reasoning. This is why LLMs can actually even have preferences, which is not the sort of thing you expect a computer program to have. Designers of LLMs wrestle with this problem in the development process, which is when they’re essentially teaching the models rather than programming them. This is why it’s sometimes described as growing rather than building an AI.\n\nYou will tend to get much better results if you lean into the humanness of LLMs. Treat AI the way you’d treat a smart intern instead of a computer program. You’d give the intern specific, clear instructions instead of assuming they know something unique to you. You’d ask them to try a task first so you can give them feedback. You’d assume they couldn’t read your mind. And you’d be (hopefully) nice to them. All of these things—even the being-nice part—have been shown to get better results from LLMs.\n\n## AIs do not remember.\n\nWith LLMs, every conversation starts fresh, as though you were talking to a person with memory-loss. Think 50 First Dates or some other movie about a person with amnesia. This is the unavoidable byproduct of how they operate, not an intentional design.\n\nChatGPT, Claude, and Gemini do have (relatively weak) ways to get their models to “remember” things by injecting what they hope are relevant details at the start of every new conversation (in a way that’s hidden from your view). These details are gleaned from past conversations. Sometimes it works and often it doesn’t. This also explains why ChatGPT might weirdly throw in a fact it knows about you but that isn’t really relevant to the current topic, and why it has no recollection of a conversation you had a couple of months ago.\n\nThe amnesia problem is why there’s so much work being done by people to create memory-style systems that LLMs can call on to help. These mostly operate by giving the AI a way to take notes and, in a later conversation, check its notes. Right now, though, there are no widely used, consumer-friendly ways to give AI better memory.\n\nSo with each new conversation, you have to give the LLM the information it needs. You can do that by just typing it in, or adding a document, or using the built-in things like projects, connectors, and skills. If you don’t know what those are, just ask ChatGPT or Claude to teach you how they work.\n\n## AIs don’t learn new things.\n\nThis next misconception is related to the memory problem. AIs have a way of being both incredibly smart and incredibly dumb, but why they’re dumb might feel mysterious.\n\nWhat AI “knows” comes from two, and only two, places:\n\n  * What the LLM learned during training by the company who made it.\n  * What it was told in the current conversation.\n\n\n\nThat’s it. What this means is that LLMs don’t really _learn anything from you_. They don’t get to know you over time, except for what the software might save as a memory. They don’t learn your habits. In fact, they don’t even know who you are beyond what they’re told about you or by you during the conversation. If it feels like ChatGPT or Claude knows you intimately, most of that intimacy is either trapped in a single, long conversation you’re having or it’s imaginary and might be just a general vibe thanks to the model’s training to be friendly to users.\n\nFor the same reason, LLMs also don’t know recent events. All models have a “knowledge cut-off date.” This is the date at which the latest training data was collected. So a model with a January 2026 cutoff date doesn’t know that the US attacked Iran in February, or that Jessie Buckley won Best Actress for _Hamnet_ , or that the US men’s team just qualified for the knock-out round in the World Cup.\n\nThe only way they can know recent facts in a new conversation is if someone or something tells them so. During the chat with you, the most common way they learn new things is by searching the web. All of the major AI companies basically give their LLMs a version of Google. This means the correct answer is only as good as the search results fed to the model. And again, because of the amnesia problem, if you start a new conversation and ask again about 2026 Oscar winners then they will need to search again for the same information.\n\n## AI drinks all the water.\n\nIt doesn’t. There are serious concerns to weigh, current and future, in a world with AI. ChatGPT taking all the drinking water is not one of them.\n\n## AI today can do far more than most people realize.\n\nLarge language models are surprisingly simple machines. They only produce one thing: text. But that makes a lot of things possible.\n\nText is how we give commands to computers. Even when you point and click, there was text (computer code) that made it work. To make AI more powerful, LLM products like ChatGPT and Claude have been trained to output text that takes the shape of software commands. This is how they make a chart for you, edit a document, check your calendar, or read your email. Basically everything done on a computer can be done by an LLM using text commands that triggers software.\n\nWithout these software tools, LLMs are nothing but chatbots. But with tools, they’re frankly amazing. (Note that Gemini is far more limited in the kinds of tools it has than you’ll get with ChatGPT and Claude.) To make the most of the available tools, do the following things:\n\n  * Download the ChatGPT or Claude applications to your computer instead of using the web browser versions. With the software actually on your computer, they can get a lot more done.\n  * Try Codex (ChatGPT) or Cowork (Claude). These are tools that make those two LLMs far more powerful. Just ask the AIs how to use these tools and for examples of what you can do with them.\n  * Explore Connectors. These are ways for your LLM to talk to other software products you use, like Gmail or Canva. When you tell ChatGPT or Claude to draft an email for you, and then you discover the draft sitting ready to be sent, it feels pretty magical.\n  * Pay for a subscription. Much of what I’ve described above is only available if you pay for at least the $20/month Pro plans. Try it for a month and see if it’s worth the money.\n\n\n\n## AI can teach you how to use it.\n\nIf any of this felt over your head, or if you’re not sure what to do next, just ask AI to teach you. If the explanation is too complicated, tell it to explain more simply. I’ve learned more in the last year than I have in any previous year of my life. (Including four years of grad school!) Claude and ChatGPT have been the teachers.\n\nOf course, people can be helpful teachers, too. The best place to learn from people about AI is currently YouTube. So I’ll leave you with this post about my favorite AI YouTube channels.",
  "title": "Some common misconceptions about LLMs",
  "updatedAt": "2026-06-22T23:14:31.628Z"
}