How language models like Claude and ChatGPT really work. Token prediction, context windows, and hallucinations explained for business owners.
Let's start from the beginning. When you open Claude, ChatGPT, or any other language model, and type "write me an email to a client" — what actually happens? How does software sitting on a server know how to write sentences that sound like a human wrote them?
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The short answer: it doesn't. At least not in the way we understand the word "know." What it does is something entirely different, and understanding that difference is what will turn you from a casual user into someone who gets ten times more value from these tools.
In this article, we'll break down the three most important concepts you need to understand how AI really works — and why it matters to you as a business owner.
Imagine you're playing a game. Someone says: "This morning I drank a cup of ____." What word is most likely to come next? Probably "coffee," right? Maybe "tea." Definitely not "screwdriver" or "table."
Now imagine someone who has read every book, every article, every website, every forum post ever published — and based on all that text, they can calculate with mathematical precision the probability of every possible word appearing after any sequence of words. That's exactly what a language model does.
When you ask Claude "what's the difference between digital marketing and traditional marketing?", it doesn't go to a database and pull out a ready-made answer. Instead, it builds the response word by word:
Each prediction is called a token — a unit of text (usually a word or part of a word). Hence the name: Next Token Prediction.
Because the amount of text the model "learned" from is enormous. We're talking trillions of words. When you've learned from all of humanity's written knowledge, you develop an incredible statistical "sense" for which sentences make sense and which don't, how humans explain things, and what good answers look like.
Practical example: When you ask "write a professional email to a client complaining about a delivery delay," the model doesn't "understand" what an angry client feels. But it's read thousands of examples of professional emails written in similar situations, and it knows that after "We apologize" usually comes "for the inconvenience" and not "for the weather." The result? An email that sounds professional and empathetic, even though nobody "felt" anything.
Think of it like an experienced chef. A chef who's cooked thousands of meals doesn't need to stop and think "how much salt should I add." Their hands just know. They don't truly "understand" food chemistry — but their vast experience creates intuition that looks like deep understanding. A language model works on the same principle, just with words instead of spices.
Now that we understand the model guesses word by word, a critical question arises: based on what does it guess? The answer is the context window — the amount of information the model can "hold in its head" at any given time.
Think of it this way: you're in a meeting with a client. If you have their entire history — what they bought before, what they asked, what their problems are — you can give them a precise, relevant answer. If you're meeting them with zero background, your answers will be generic and less useful.
That's exactly how AI's context window works.
Everything in the current conversation:
Different models have different context window sizes. Anthropic's Claude, for example, supports a window of 200,000 tokens — that's roughly 500 pages of text. The implication? You can upload an entire 100-page document and ask questions about it, and it will "hold" the whole document in mind.
Practical example: Say you want Claude to write marketing content for you. If you just say "write a post about my business" — the result will be generic. But if you provide context: "I run a hair salon in Tel Aviv, my audience is women ages 25-45, my unique selling point is organic-only products, here are 3 happy customer reviews" — now the model will predict much more relevant words, because its context is richer.
This is one of the most common mistakes in using AI. People ask short questions and expect perfect answers. But it's like going to a doctor and saying "it hurts" without mentioning where, when it started, or what makes it worse. The more context you provide, the more accurate the answer will be.
This is exactly why when working with Claude Code, for example, it works directly on your files — because that way it has the full context of your project, not just what you tell it.
Now that we understand the first two concepts, the third one makes perfect sense. A hallucination is when the model makes up information with full confidence — as if it were a proven fact.
Why does this happen? Because the model doesn't actually "know" things. It guesses words. And if statistics say the sentence "The book 'Thinking, Fast and Slow' was written by Daniel Kahneman and published in 2011" sounds logical — it will write that, whether it's true (in this case, yes) or not.
Remember the context window? When the model has enough relevant information, its statistics are accurate because it's guessing based on real facts in front of it. But when it doesn't have information — it doesn't say "I don't know." Instead, it keeps guessing, and this is where statistical prediction can turn from "logical pattern" to "convincing fiction."
Practical example: If you ask Claude "what are the opening hours of Dana's Hair Salon at 15 Herzl Street in Tel Aviv?" — it has no way of knowing this. But statistics say salons are typically open Sunday-Thursday, 9:00-19:00, Friday 9:00-14:00. So it might give those hours with full confidence — even though it made them up.
Now that we understand all three concepts, we can build a simple mental model:
Or simply:
Good context + proper usage = amazing results
Missing context + uncalibrated expectations = disappointment and problems
A new employee on their first day — even a genius — needs onboarding. They need to know who the clients are, what the company style is, what's been done before. That's exactly how you should treat AI. Give it context, guide it, and don't expect it to know everything on its own.
Don't start by writing legal contracts. Start with email drafts, content ideas, meeting summaries. Learn the tool, understand its strengths and weaknesses, then expand gradually.
AI is a tool, not an expert. Its output is a first draft, not a finished product. Read it, edit it, and verify the information before sending it to clients.
If the answer isn't good, before giving up — rephrase your question. Add details, change the angle, give an example of what you want. Sometimes a small change in the request creates a dramatic change in the result.
AI is not magic and it's not an electronic brain. It's an incredibly sophisticated word-guessing machine that works best when given precise context and when its output is reviewed. This understanding is what separates those who are disappointed by AI ("it doesn't work") from those who use it every day to save hours and create real value for their business.
And if you want to learn how to use these tools practically — that's exactly what our AI workshops are for. Not dry theory, but hands-on work with the tools, on your actual business problems.
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