How AI Models Actually Work (In Plain English)

The Magic Black Box Problem

When I first started using AI tools like ChatGPT and Claude, I felt like I was talking to some kind of digital wizard. I'd type a question, hit enter, and somehow this thing would understand exactly what I meant and give me a thoughtful response. It felt like pure magic.

But here's the thing that bugged me: I had no idea how it actually worked. And when you don't understand how something works, it's hard to use it effectively. You end up feeling like you're just throwing requests into a black box and hoping for the best.

So I went down a rabbit hole to figure out what's actually happening under the hood. Today, I want to share what I learned in the simplest terms possible—no computer science degree required.

Think of AI Like a Really Smart Autocomplete

The easiest way I've found to understand AI models is to think of them as incredibly sophisticated autocomplete systems. You know how when you start typing on your phone, it suggests the next word? AI models work on the same basic principle, just cranked up to eleven.

When you ask ChatGPT "What's the weather like?", it's essentially thinking: "Given all the text I've seen before, what words should come next after this sequence?" It's predicting the most likely continuation based on patterns it learned from millions of examples.

The difference is that instead of just suggesting "today" or "tomorrow", it can generate entire coherent responses that actually make sense in context. But at its core, it's still just really good at guessing what comes next.

Tokens: How AI Sees Your Words

Here's where things get interesting. AI models don't actually read your text the way you do. They break everything down into chunks called "tokens." Think of tokens as the bite-sized pieces that AI can actually digest.

Most of the time, one token equals roughly one word. But it's not always that simple. Sometimes a single word gets split into multiple tokens, and sometimes multiple characters get grouped into one token. For example:

Notice how some tokens include the space before the word? That's because the AI needs to understand not just the words, but how they're separated and formatted.

Why does this matter? Because AI models have limits on how many tokens they can work with at once. It's like they have a specific amount of "attention span" measured in tokens.

Context Windows: The AI's Working Memory

This brings us to one of the most important concepts: context windows. Think of this as the AI's working memory—how much information it can keep track of in a single conversation.

When I first learned about this, it was like a lightbulb moment. Have you ever noticed that sometimes AI seems to "forget" things you mentioned earlier in a long conversation? That's because you've exceeded its context window.

Different models have different context window sizes:

To put this in perspective, a typical email might be 100-200 tokens. A blog post like this one is probably around 1,500 tokens. So even the "smaller" context windows can handle quite a bit.

How AI Actually Generates Text

Now for the really fascinating part: how does the AI actually create its responses? This was the piece that took me the longest to wrap my head around.

The AI doesn't plan out its entire response before writing it. Instead, it generates text one token at a time, from left to right, making decisions about what comes next based on everything that came before.

Picture this process:

Step 1: You ask "What's the capital of France?"
Step 2: The AI looks at your question and thinks "What's the most likely first word of a good response?" It might choose "The"
Step 3: Now it thinks "Given the question and that I started with 'The', what comes next?" It chooses "capital"
Step 4: This continues: "of" → "France" → "is" → "Paris" → "."

At each step, the AI is calculating probabilities. It's not just picking the single most likely next word—it's using those probabilities to make choices that create varied, interesting responses.

Why This Knowledge Makes You Better at AI

Understanding these basics has completely changed how I interact with AI tools. Here's why this matters for your day-to-day use:

Better Prompts: Knowing that AI predicts what comes next helps you write prompts that guide it toward better responses. Instead of "Write about dogs," try "Here's a comprehensive guide to choosing the right dog breed:" and let the AI complete that thought.

Managing Long Conversations: Understanding context windows helps you know when to summarize or start fresh instead of wondering why the AI seems confused.

Realistic Expectations: Knowing that AI generates text step-by-step helps explain why it sometimes contradicts itself or goes off-topic—it's not planning ahead like humans do.

The Bottom Line

AI isn't actually "thinking" the way we do. It's an incredibly sophisticated pattern-matching system that's gotten really good at predicting what text should come next. It breaks your input into tokens, considers everything within its context window, and generates responses one piece at a time.

Does this make AI less impressive? I don't think so. If anything, it makes it more fascinating. The fact that this relatively simple concept—predict the next word—can create such seemingly intelligent behavior is pretty amazing.

Plus, understanding how it works gives you superpowers as a user. You can craft better prompts, manage conversations more effectively, and know what to expect from these tools.

The magic black box isn't quite so mysterious anymore. And that's exactly where we want to be.