No Feelings, No Memory: The Illusions and Realities of LLMs
🧠 We all know ChatGPT—but what exactly is an LLM?
These days, many people have tried using ChatGPT and experienced chatting with AI firsthand. Reactions like “Wow, it talks just like a person!” are common. But then come the questions: “How is this even possible?” or “What’s the technology behind ChatGPT?”
Well, ChatGPT is powered by a technology called an LLM. That stands for Large Language Model—an enormous AI system trained to understand and generate human language.
🤖 It’s not just ChatGPT: Meet other LLMs
LLMs aren’t exclusive to ChatGPT. Many of today’s AI tools that understand and use language are built on their own versions of LLMs. For example:
- Claude – developed by Anthropic
- Gemini – Google’s language model (formerly known as Bard)
- LLaMA – Meta’s open-source model
- Other models like Mistral, Command-R, Perplexity, and Pi
Each of these AIs uses a different LLM trained in its own way to answer questions, write content, and summarize information. In other words, LLMs are the “brains” behind today’s language AIs—and they’re the key technology driving this new AI era.
📱 Snartphone Autocomplete and 🧠 LLMs Work in Similar Ways
To understand how LLMs actually work, it helps to think about something familiar: the autocomplete feature on your smartphone.
Let’s say you’re texting a friend and you type, “Do you want to…”—your phone might suggest:
- “...grab dinner?”
- “...hang out tonight?”
- “...go for a walk?”
This feature works by predicting what you’re likely to say next, based on what you’ve typed in the past and common sentence patterns used by many people.
LLMs work in a very similar way. The difference? They do it on a massive and incredibly sophisticated scale.
🧠 Does AI Really Remember Me?
😮 “Wait… is it actually remembering what I said?”
If you’ve chatted with ChatGPT for a few days, this thought might have crossed your mind:
“Whoa, it remembers what I told it last time!”
“It seems to know my preferences now.”
“I don’t even need to explain again—it just gets it!”
At that point, a curious question pops into your head:
“Could it be… is this thing actually remembering me?”
Sadly (or maybe reassuringly?), here’s the truth:
AI is like a genius with amnesia.
🧂 It only seems like it remembers—here’s the trick
At its core, ChatGPT doesn’t actually have memory.
Even the conversation you're having right now will vanish the moment you close the chat. It’s like talking to someone with short-term memory loss.
So then why does it feel like it remembers?
Because it’s pulling off a really clever trick:
- It analyzes everything you've said in the current conversation, all at once,
- and uses that context to act as if it remembers.
In other words, the AI isn’t “remembering” what you said earlier—it’s just looking at all the messages in front of it at the same time and responding accordingly.
It’s a bit like cramming for a test right before walking into the room—you remember everything for now, but once it’s over? Poof. It’s gone.
🧠 Then why does it feel so smart?
Just because GPT can’t remember long-term doesn’t mean it’s clueless. It’s incredibly good at picking up on what you’re saying and understanding the context—using statistics in surprisingly sophisticated ways.
🏗️ A Giant Autocomplete Machine: The Principle of LLMs
LLMs are trained on billions of pieces of text, learning patterns like:
“What kind of word usually comes next in this kind of sentence?”
For example:
- “Do you want to…” → most likely next word: grab
- “Do you want to grab…” → next word might be: dinner
It builds sentences one word at a time by predicting what comes next. That’s what an LLM is at heart—a machine built to predict the next word.
✨ Real-World Uses of LLMs
Because LLMs are so good at language prediction, we can use them for all sorts of tasks:
- Polishing emails
- Summarizing news articles
- Writing code
- Translating between languages
- Planning travel itineraries
- Generating content ideas, and more
All of these start with one core ability: guessing the next word with amazing accuracy.
🪄 It feels like magic, but it’s really math and statistics
This may feel like magic, but it’s all based on math—huge-scale statistical calculations across massive datasets of text.
LLMs analyze your questions, understand the tone and context, and then generate plausible responses automatically. You can think of them as supercharged autocomplete engines.
🔍 One last question: Is it actually telling the truth?
And here’s the important part:
LLMs are great at sounding convincing—but that doesn’t mean what they say is always true.
Sometimes, they present totally made-up information in a way that sounds completely believable.
This is what we call AI hallucination—when a model generates content that sounds right but is factually wrong.
📘 Coming up next: The Limits of LLMs – Understanding AI’s Illusions
In the next post, we’ll dive into why these hallucinations happen—and how we can use AI more responsibly and carefully.