What are Large Language Models?
What are Large Language Models?
Show Notes
What we talk about
In this episode we explore Large Language Models: what they are, how they work, and why they sometimes get things wrong.
Key Points
- How they work: LLMs predict the next word, like phone autocomplete but at massive scale
- What they’ve “read”: Billions of web pages, books, articles, and conversations
- They don’t truly understand: They have statistical intuition about language, not real comprehension
- Hallucinations: They generate text that sounds good but may be completely false
- When to use them: Perfect for writing, brainstorming, code. Always verify facts.
Transcript
Welcome to FIVE-minutes-AI. I'm Luca, and in this series I explain artificial intelligence in simple terms, no jargon. Five minutes, one concept. Today: what the heck are these Large Language Models everyone keeps talking about.
You know when you're typing a message on your phone and it suggests the next word? "Hey, how..." and your phone suggests "are". Well, a Large Language Model does exactly that. But instead of suggesting one word, it suggests thousands. One after another. Building entire responses.
The name says it all: Large Language Model. "Large" because it has read a monstrous amount of text — we're talking billions of web pages, books, articles, conversations. "Language" because it works with language, with words. "Model" because it's a mathematical model that learned patterns from all that text.
Imagine you'd read every book ever written, every Wikipedia article, every online discussion. After all that, you'd develop an intuition for how language works. You'd know that after "The cat sat on the..." probably comes "couch" or "carpet", not "airplane". An LLM has done exactly that: it developed a statistical intuition for language.
But here's the thing — and this is crucial — an LLM doesn't actually understand. It doesn't think. It's incredibly good at predicting which words make sense together. It's the difference between a very smart parrot and a person. The parrot can repeat perfect sentences without understanding their meaning.
This explains some weird things you might have noticed. Sometimes ChatGPT or Claude give you answers that seem perfect but are completely wrong. In the jargon, we call these "hallucinations". The LLM is generating words that sound good together, but it's not checking if they're true. It's like an actor improvising a doctor's speech: sounds convincing, but they never studied medicine.
So what are they good for? They're extraordinary tools for certain tasks. Writing, summarizing, translating, explaining concepts, brainstorming, generating code. They're terrible for others: precise calculations, remembering specific facts, being always accurate.
The trick is knowing when to use them and when not to. If you need creativity, writing assistance, a first draft of something: perfect. If you need absolute certainty about a fact: always verify.
To recap: a Large Language Model is a system that learned to predict language by reading enormous amounts of text. It doesn't understand, doesn't think, but it's incredibly useful if you know how to use it.
In the next episode, I'll explain the differences between ChatGPT and Claude — and why you'd use one or the other for different tasks.
I'm Luca, this was FIVE-minutes-AI. See you tomorrow.