How ChatGPT Actually Works: Explained for Non-Engineers

My dad asked me last week: "How does ChatGPT know things? Is someone typing on the other end?"

It's a fair question. When you chat with ChatGPT, it feels like you're talking to something intelligent. It understands context, remembers what you said earlier in the conversation, and responds in ways that seem thoughtful and creative. If you didn't know better, you might assume there's a person behind it, or at least some kind of sentient intelligence.

There isn't. What's actually happening is simultaneously more fascinating and more mundane than most people realize.

I'm not an engineer, but I've spent the last few months trying to understand how this technology actually works. Not the dense math and code – the conceptual explanation that makes sense to regular people. Here's what I've learned.

The simplest possible explanation

Here's the core concept in one sentence: ChatGPT predicts the next word.

That's it. That's fundamentally what it does. When you send a message, ChatGPT looks at all the words you wrote, then predicts what word should come next in the response. Then it predicts the word after that. And the word after that. One word at a time, building a response by repeatedly asking itself "what word probably comes next?"

"But wait," you're thinking, "that can't be right. ChatGPT writes coherent essays, solves problems, translates languages. That's way more sophisticated than predicting the next word."

And you're both right and wrong. The sophistication comes from how incredibly good it's gotten at this one task of predicting what comes next. Good enough that coherent essays, problem-solving, and translations emerge from this basic process.

Let me explain how that's possible.

The autocomplete comparison everyone uses (because it actually helps)

You know how your phone keyboard suggests the next word as you type? That's the same basic concept as ChatGPT, just vastly more sophisticated.

When you type "I'm going to the..." your phone might suggest "store" or "office" or "gym." It's predicting likely next words based on common patterns in language.

ChatGPT is doing something similar, but on a scale that's hard to comprehend. Your phone's autocomplete is working with maybe a few hundred thousand common phrases. ChatGPT was trained on hundreds of billions of words – essentially most of the public internet, thousands of books, and massive text datasets.

The difference in scale creates a difference in capability. Your phone's autocomplete can suggest the next word. ChatGPT can predict the next word so well that it can write an entire essay, one word at a time, that makes sense from beginning to end.

Here's a simple example. If I give ChatGPT the prompt "The capital of France is..." it predicts "Paris" not because it has a database of capitals, but because in all the text it was trained on, the word "Paris" appears after "The capital of France is" vastly more often than any other word.

Now make this infinitely more complex, with nuance, context, and understanding of how language works at a deep level, and you start to get ChatGPT.

What does "trained on text" actually mean?

When people say ChatGPT was "trained," what does that mean?

Imagine you had to learn a language by reading millions of books but never getting explicit grammar lessons. You'd start noticing patterns. After seeing thousands of sentences like "The dog ran quickly" and "The cat moved swiftly," you'd learn that words describing how actions happen usually come after the verb. You wouldn't know the term "adverb," but you'd understand the pattern.

That's conceptually what happened with ChatGPT, except at a massive scale.

The training process involved feeding GPT massive amounts of text and essentially asking it, over and over and over: "Given these words, what word comes next?" It would guess. Sometimes it was wrong. When it was wrong, the system adjusted to make better predictions next time.

This happened billions of times. The system gradually learned patterns in language – grammar, facts, reasoning styles, even how to structure arguments.

The key insight: it learned these patterns by exposure, not by being explicitly programmed. Nobody taught ChatGPT the rules of grammar or fed it a database of facts. It discovered these patterns by processing enormous amounts of text and learning to predict what comes next with increasing accuracy.

Why it seems to "understand" when it's just predicting

Here's where it gets interesting. If you're good enough at predicting what comes next in language, you start to appear intelligent.

Think about it: if I'm in the middle of explaining something complex, and you can accurately predict how I'll finish that explanation, you must understand the subject matter. You can't predict my next words without understanding the context, logic, and content of what I'm saying.

ChatGPT does this at scale. It predicts next words so accurately that it has to understand (at some level) grammar, facts, reasoning, cause and effect, and how humans structure information.

Is this "real" understanding? Philosophers and AI researchers debate this. But functionally, for practical purposes, the system behaves as if it understands. Ask it why the sky is blue, and it will generate an accurate explanation not by looking up a stored answer, but by predicting the words that would typically appear in an explanation of that phenomenon.

I tested this with a weird question: "Explain quantum physics using only cooking metaphors." ChatGPT generated a creative response comparing quantum states to recipe ingredients, wave functions to mixing techniques, and observation effects to checking on food in the oven.

It didn't have that specific explanation stored anywhere. It predicted words that would create a coherent response matching the unusual request, drawing on its learned patterns about both quantum physics and cooking.

The "transformer" architecture (without the scary math)

ChatGPT is built on something called a "transformer" – that's the "T" in GPT (Generative Pre-trained Transformer). You don't need to understand the technical details, but the key innovation is worth knowing about.

Earlier AI systems processed text sequentially, like reading a book word by word from left to right. The transformer architecture does something different: it processes all the words at once and figures out how they relate to each other.

Here's why that matters. In the sentence "The animal didn't cross the street because it was too tired," what does "it" refer to? The animal, obviously. But in "The animal didn't cross the street because it was too wide," "it" refers to the street.

Understanding these relationships – how words in different parts of a sentence relate to each other – is crucial for language understanding. Transformers are really good at this.

This is what allows ChatGPT to maintain context over long conversations. It's constantly analyzing how all the words relate to each other, not just processing them one by one.

Why it sometimes gets things confidently wrong

If ChatGPT is so good at predicting text, why does it occasionally make obvious mistakes or confidently state incorrect information?

Because it's predicting what words would typically appear in response to your question, not accessing a database of facts. Sometimes the most likely-sounding answer isn't the correct answer.

If you ask ChatGPT something obscure that wasn't well-represented in its training data, it will still generate a response by predicting what words would likely appear in an answer to that type of question. The response might sound authoritative but be completely wrong.

This is called "hallucinating" – generating plausible-sounding information that's actually false. It's not lying or trying to deceive you. It's doing what it was designed to do: predicting the next words that would typically appear in this context. Sometimes those predicted words form false statements.

I asked ChatGPT about a fictional book I made up, describing it as if it were real. ChatGPT generated a detailed summary, analysis, and even "quotes" from this book that doesn't exist. It predicted what words would appear in a book summary because that's what my prompt was asking for, even though the book was fake.

This is why you shouldn't use ChatGPT for anything where accuracy is critical without verifying the information. It's not a search engine or encyclopedia. It's a prediction engine that's very good at generating plausible text.

What happens when you send a message

Let's walk through what actually happens when you type something and hit send.

Step 1: Your message gets broken into "tokens"

ChatGPT doesn't process whole words exactly. It breaks text into "tokens" – chunks that might be words, parts of words, or punctuation. "Hello" might be one token. "unbelievable" might be broken into "un," "believ," and "able."

This tokenization lets it handle any text, including made-up words or words in different languages.

Step 2: Tokens become numbers

Computers don't understand words, so each token gets converted into numbers. This creates a mathematical representation of your message that the AI can process.

Step 3: The model analyzes relationships

The transformer architecture analyzes your message, figuring out how different parts relate to each other. It's building understanding of what you're asking and what context matters for the response.

Step 4: Predicting begins

ChatGPT starts generating a response by predicting the first token. Based on all its training, all your previous messages in the conversation, and the analysis of relationships between words, it calculates probability distributions for what token should come first.

It doesn't just pick the most likely token. There's some randomness involved – it samples from the probability distribution. That's why if you ask the same question twice, you might get slightly different responses.

Step 5: Repeat, repeat, repeat

Once it's predicted the first token, it predicts the second token based on your message plus the first token it just generated. Then the third token based on your message plus the first two tokens. And so on.

Each token prediction considers all the previous tokens, maintaining coherence and context as it builds the response.

Step 6: Stopping

Eventually, ChatGPT predicts a token that indicates the response is complete (basically a "stop" signal), and it stops generating. Your response appears.

This entire process happens in seconds, even though it's predicting potentially hundreds of tokens, each requiring complex calculations considering all previous context.

Why context windows matter

You might have heard about "context windows" – the amount of text ChatGPT can consider at once. Why is this a limitation?

Because analyzing relationships between words is computationally expensive. The longer the text, the more relationships to analyze. There's a practical limit to how much text can be processed at once.

Older versions of ChatGPT had relatively small context windows – a few thousand words. Newer versions can handle much more, but there's still a limit.

When you exceed the context window, ChatGPT starts "forgetting" earlier parts of the conversation. It's not losing memory like a person might; it literally can't process text beyond the window size. The oldest messages drop out of context.

This is why in very long conversations, ChatGPT might forget something you mentioned way back at the beginning. It's not being forgetful – that information is no longer in its processing window.

It doesn't actually "know" anything

Here's a mind-bending aspect: ChatGPT doesn't store facts the way a database does. It doesn't have a list of capitals or historical dates or scientific principles.

Instead, it has billions of numerical parameters (think of them as knobs and dials) that were adjusted during training to make accurate predictions. Facts are encoded implicitly in these parameters.

When you ask "What's the capital of France?" ChatGPT doesn't look up the answer. It generates "Paris" because those specific parameters, in that configuration, lead to predicting "Paris" as the most likely word to follow your question.

The knowledge is distributed across the entire model. You can't point to a specific place where the "capital of France" is stored. It emerges from the pattern of billions of parameters working together.

This is why ChatGPT can sometimes get basic facts wrong – the fact wasn't explicitly stored anywhere; it's a pattern that emerged from training, and patterns can be imperfect.

Why newer versions are better

GPT-4 is better than GPT-3.5, which was better than GPT-3. Why? Three main reasons:

More parameters: Bigger models with more parameters can capture more nuanced patterns. GPT-4 has significantly more parameters than GPT-3.5 (the exact number isn't public, but it's substantially larger).

Better training data: Newer versions were trained on more text, better-curated text, and more recent text.

Improved techniques: The basic architecture is similar, but researchers have developed better training methods, ways to handle edge cases, and techniques to reduce errors.

The fundamental mechanism – predicting the next word – is the same. But doing it at larger scale with better data and improved techniques creates noticeably better results.

What ChatGPT can't do (and why)

Understanding how it works helps explain its limitations.

It can't truly reason or plan. It generates responses token by token without a big-picture plan. Sometimes this creates responses that start one way and end up somewhere inconsistent. It's not thinking ahead; it's predicting forward.

It can't access real-time information. Unless it's using a web search tool, ChatGPT only knows what was in its training data, which has a cutoff date. It can't tell you what happened yesterday.

It can't learn from conversations with you. Each conversation might feel like it's learning, but it's not updating its parameters based on what you teach it. The next user who asks the same question gets the same wrong answer you just corrected.

It can't do complex math reliably. Despite seeming smart, ChatGPT is predicting text, not calculating. It might generate math that looks right but isn't. (This is improving with tools like code interpreters, but the base model isn't great at math.)

It can't truly understand like humans do. This is philosophical, but worth noting. ChatGPT predicts patterns it learned from text. Whether that constitutes "real" understanding is debatable.

The difference between ChatGPT and search engines

People sometimes confuse ChatGPT with Google. They're fundamentally different.

Google finds existing information on the internet and shows you where it's located. It's like a librarian directing you to the right book.

ChatGPT generates new text based on patterns it learned. It's not finding and retrieving information; it's creating text that matches patterns in its training data.

This is why Google citations link to sources, while ChatGPT's answers are generated on the fly. Google is searching; ChatGPT is synthesizing.

Both are useful for different purposes. Google for finding specific current information and sources. ChatGPT for explanations, brainstorming, creative tasks, and working with information in flexible ways.

Is it actually intelligent?

This is where things get philosophical. ChatGPT displays behaviors we associate with intelligence – it answers questions, solves problems, writes creatively, seems to understand context.

But it's doing this through pattern prediction, not through anything resembling human consciousness or understanding. It doesn't have beliefs, desires, or awareness. It's not thinking about your question; it's calculating probability distributions for next tokens.

Some researchers argue this doesn't matter – if the behavior is intelligent, the mechanism is irrelevant. Others argue there's a fundamental difference between genuine understanding and very sophisticated pattern matching.

For practical purposes, it doesn't matter much. ChatGPT is useful for many tasks regardless of whether its "intelligence" is "real." But it's worth keeping in mind that the intelligence you're interacting with is qualitatively different from human intelligence.

My take: it's smart like a calculator is smart at math. The calculator doesn't "understand" mathematics, but it reliably produces correct mathematical results. ChatGPT doesn't "understand" in a human sense, but it reliably produces useful text based on sophisticated pattern recognition.

Why it feels like conversation

Despite being a prediction engine, ChatGPT creates the experience of conversation. Why?

Because human conversation itself is partly about predicting. When you talk to someone, you're constantly predicting what they mean, what they might say next, how they'll react to your words. Good conversation involves anticipating and responding to these predictions.

ChatGPT is very good at predicting conversational patterns. It knows when to ask clarifying questions, when to provide examples, when to acknowledge confusion, when to be formal or casual. These patterns were in its training data – billions of examples of human conversation.

So it feels conversational because it learned the patterns of conversation and replicates them through prediction. It's not conscious conversation, but it's effective simulation of conversation.

The prompt engineering rabbit hole

Since ChatGPT is predicting based on input, how you phrase your question matters enormously. This has spawned "prompt engineering" – crafting prompts to get better responses.

Simple example: "Write an email" gets okay results. "Write a professional but friendly email to a client explaining a project delay, acknowledging the inconvenience, providing a new timeline, and ending on a positive note" gets much better results.

The second prompt gives ChatGPT more context for prediction. It can predict more accurately what words should appear in that specific type of email.

This is why people share "prompt templates" and why there are now courses on prompting. The system is powerful, but accessing that power requires knowing how to frame requests effectively.

What this means for the future

Understanding how ChatGPT works – next-word prediction at massive scale – helps us think about where this technology is going.

Limitations are partly about scale. Many current limitations might be solved by training bigger models on more data. Not all limitations, but many.

It's not artificial general intelligence. Despite impressive capabilities, this architecture probably isn't the path to human-level AI. It's very good at language tasks but doesn't think, plan, or reason the way humans do.

The technology is still evolving fast. Researchers are finding ways to make prediction more accurate, handle longer contexts, reduce errors, and expand capabilities. GPT-5 will be better than GPT-4, just like GPT-4 was better than GPT-3.

Integration is key. ChatGPT becomes more capable when connected to other tools – web search, calculators, code execution, image generation. The prediction engine at the core is powerful, but combining it with other capabilities creates something even more useful.

Should you trust it?

Now that you understand how it works, here's my advice on trust:

Trust it for brainstorming, explaining concepts, writing drafts, creative tasks, and coding help. These leverage its strengths – generating useful text based on learned patterns.

Don't trust it blindly for facts, math, legal/medical advice, or anything where accuracy is critical. Verify important information, especially facts that could be wrong.

Think of it as a very knowledgeable colleague who's sometimes confidently wrong. You'd trust their input and expertise but check important details before relying on them completely.

Understanding that it's predicting text, not accessing truth, should inform how you use it. It's an amazing tool, but tools require knowing their limitations.

FAQ

Wrap up

You don't need to understand transformers, neural networks, or the technical details to use ChatGPT effectively. But knowing the basic concept – sophisticated next-word prediction trained on massive text data – helps you understand what it's good at, why it sometimes fails, and how to work with it productively.

It's not magic. It's not sentient. It's not searching a database of facts. It's predicting what words come next, based on patterns learned from billions of examples of human text.

That simple mechanism, executed at massive scale with enormous computational power, creates something that feels remarkably intelligent and is genuinely useful for countless tasks.

The more people understand how it actually works, the better they can use it, the more realistic their expectations, and the less likely they are to be either overly afraid of AI or overly trusting of its outputs.

It's just predicting the next word. But sometimes, that's enough to feel like magic.

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