Large Language Models Explained Without the Jargon

If you've read anything about AI in the last two years, you've probably tripped over the phrase "large language model" - usually shortened to LLM. It sounds technical. It isn't, really. Let's take the jargon apart in a way you could explain to a friend over coffee.

What is a large language model, in one sentence?

A large language model is a computer program that has read an enormous amount of text and, based on all that reading, has gotten very good at predicting what words should come next.

That's it. That's the whole thing. When you type a question into ChatGPT, it isn't "thinking" the way you do. It's guessing - with astonishing accuracy - what a sensible, helpful response would sound like, one word at a time, based on the patterns it learned from billions of pages of text. According to MIT Technology Review, this next-word prediction is genuinely all that's happening under the hood - the surprise is just how much useful behavior falls out of doing it well.

Why does that matter for your business? Because once you understand that an LLM is a very sophisticated pattern-matcher on text, three other jargon words suddenly make sense: tokens, prompts, and context windows.

Tokens: the AI's version of syllables

An LLM doesn't see words the way you do. It breaks language into chunks called tokens. A short word like "cat" is one token. A longer word like "canvassing" might be two or three. Punctuation counts too.

Think of tokens like the syllables an auctioneer rattles off - the model processes language in these little chunks, not in whole sentences. This matters for one practical reason: every AI vendor charges by the token. Longer conversations, longer documents, longer answers - all more tokens, all more cost. It's roughly like a taxi meter that runs on words instead of miles. Token-based pricing is one of the biggest reasons AI project costs surprise businesses that roll it out without watching usage - the meter runs even when the ride isn't going anywhere useful.

Prompts: the instructions you hand it

A prompt is just the text you send to the model. Your question, plus any instructions or background you include with it.

Here's the part most people miss: the model has no idea who you are, what your business does, or what happened yesterday. Every time you talk to a raw LLM, it's like talking to a brilliant temp on their first day - extremely capable, zero context. If you don't tell it your pricing rules, it'll invent something plausible. If you don't tell it your service area, it'll guess.

This is why "prompt engineering" became a job title for a minute. It's really just: learning to give clear, complete instructions to something that takes you very literally.

Context windows: the AI's short-term memory

The context window is how much text the model can hold in its head at once - the prompt you sent, plus any background documents, plus the conversation so far.

Think of it as a whiteboard. Everything relevant to the current conversation has to fit on that whiteboard. Once it's full, older stuff gets erased to make room. Modern models have big whiteboards - hundreds of pages of text - but they're still finite. And critically, when the conversation ends, the whiteboard gets wiped. The model doesn't remember you tomorrow.

This is the single biggest thing business owners misunderstand about AI. Most disappointing AI rollouts fail here - the tool is smart but has amnesia, so it can't build on what happened last week, last month, or with this specific customer.

How this shapes a useful AI setup

Once you see the picture - an amnesiac genius that reads text, charges by the syllable, and needs everything relevant on the whiteboard - the approach that works makes obvious sense.

Give the AI a permanent memory it can pull from: a "second brain" full of the business's rules, pricing, notes from past conversations, decisions made, and things learned. When the AI is asked to do something, the relevant parts of that second brain get loaded onto the whiteboard before the model responds. So it never has to guess. It answers like someone who actually works at the business, because - functionally - it does.

That's the difference between "we plugged in ChatGPT" and an AI system that behaves consistently, quotes the right prices, follows your rules, and remembers what it did yesterday. Deloitte's work on enterprise AI adoption keeps landing on the same conclusion: the model isn't the moat, the context around it is.

The takeaway

LLMs aren't magic and they aren't mysterious. They're pattern-matchers on text. What makes them useful for a specific business is everything you build around them - the memory, the rules, the guardrails, the connection to your real systems.

If you want more plain-English pieces like this, the blog has a Friday digest that sums up each week's posts.