Everyone uses it. Almost nobody can explain it.
You ask AI to write your email. It does. You ask it a random trivia question. It answers — confidently, instantly, sometimes completely wrong. And somewhere in the back of your mind you think: what is actually happening in there?
Nobody sat you down and explained it. Not really. You got buzzwords — "machine learning," "neural networks," "trained on data." But buzzwords aren't explanations.
This post is the real explanation. How AI works — built on things you already understand, one analogy at a time. Let's decode it, bit by bit!
At A Glance
- What AI Actually Is: Forget the Terminator, here's the truth
- How AI Learns: The training process explained without a textbook
- Neural Networks: The architecture behind every AI you've ever used
- Large Language Models: What's really happening when you type a question
- AI vs Machine Learning vs Deep Learning: The confusion ends here
- Why AI Hallucinates: It's not a bug. It's the whole design.
- FAQ: Your top questions about how AI works, answered
What AI Actually Is: Forget the Terminator
Most people carry a completely wrong picture of AI in their heads.
They imagine something that thinks. Something that wants things. A digital brain sitting inside a data center, reasoning its way to answers.
That's not it. Not even close.
Here's a better picture. Imagine the world's most diligent student. This student has read every article ever published on the internet. Every Wikipedia page. Every novel. Every Reddit thread. Every scientific paper. Every news story — going back decades.
But here's the thing. This student has never left the library. They have never tasted food. Never been rained on. Never felt nervous. They have read about all of those experiences. In extraordinary detail. Across millions of sources.
Now you ask them: "What does rain feel like?"
They can write you a 2,000-word essay. Because they've read thousands of descriptions. But they have never been wet.
That is AI. It isn't thinking. It isn't experiencing. It is recognising patterns across an unimaginably large amount of text — and using those patterns to generate responses that sound like thinking.
Artificial intelligence (AI) is software that finds patterns in large amounts of data and uses those patterns to make predictions — generate text, recognise images, translate languages. No magic. No consciousness. Just pattern-matching at a scale no human brain can match.
How AI Learns: The Training Process Without the Textbook
You learned to ride a bike by trying. Falling. Adjusting your balance slightly. Trying again. You did not read a physics textbook about angular momentum first. You just did it until your body figured it out.
AI learns the exact same way — except instead of a bike, it's working through billions of words. And instead of scraped knees, it gets a mathematical penalty for wrong guesses.
Here's what the training process looks like, stripped down:
- The AI is shown a massive amount of data — text, images, audio, whatever it needs.
- It makes a prediction. For a language model, that's usually: what word comes next?
- It checks if it was right.
- If it was wrong, every connection inside it adjusts — slightly. Like turning thousands of tiny dials at once.
- It runs this process billions of times. Each individual adjustment is tiny. The total result is everything.
This is called training. The data used to do it is called training data.
ChatGPT was trained on an estimated 570 gigabytes of text. That's roughly 3 million books' worth. Read in one concentrated burst of computation.
What comes out the other side isn't a mind that understands language. It's a system that knows, with high confidence, what words tend to follow other words. In context. Across an enormous range of topics.
You now understand something most people don't: AI doesn't have opinions. It has probabilities.
Neural Networks: The Architecture Behind How AI Works
Here's where the real machinery is. And I promise, you already understand the concept.
Think of a telephone switchboard from an old film. Dozens of operators. Hundreds of cables. Each operator decides: does this signal pass through, or does it stop here?
A neural network is that switchboard — but with billions of connections instead of hundreds. And instead of human operators making decisions, it's mathematics.
Every connection in the network has a weight. Think of it as a volume dial. Turn it up and that connection contributes more to the final answer. Turn it down and it barely registers. Training is nothing more than adjusting billions of these dials, over and over, until the network reliably produces accurate outputs.
The human brain has roughly 100 billion neurons. GPT-4 — the model behind ChatGPT — is estimated to have around 1.8 trillion parameters. That's 1.8 trillion volume dials, each one individually tuned during training.
Your brain built those neurons over years of lived experience. AI had its dials tuned in weeks of intense computation, running on warehouses full of specialised chips.
The result isn't intelligence in any meaningful sense. It's a very well-calibrated set of dials. That turns out to be incredibly powerful.
Large Language Models: What's Happening When You Ask AI a Question
You type a question. You hit enter. A response appears. What just happened?
Think about how an experienced chef adjusts a recipe on the fly. They don't measure everything — they taste, they estimate, they adjust based on years of cooking the same dish. They're not looking up the answer. They're constructing it in real time from experience.
A Large Language Model (LLM) — the technology inside ChatGPT, Gemini, Claude — does something structurally similar with words.
Every word you type gets converted into a set of numbers. Not randomly — these numbers encode meaning. The word "cat" sits numerically close to "dog." "Hot" sits numerically far from "cold." "King" has a predictable mathematical relationship to "Queen." This numerical space is called an embedding.
The model takes your entire input, maps it into this numerical space, and then asks one question repeatedly: what word is most likely to come next?
It answers that question. Then does it again for the next word. Then the next. Until the response is complete.
That's it. That's the whole mechanism. Word by word. Probability by probability.
This is why the same model can write a poem, fix your code, summarise a document, and explain quantum physics — all with one underlying system. Every task reduces to the same question: what word comes next?
AI vs Machine Learning vs Deep Learning: The Confusion Ends Here
These three terms get used as if they mean the same thing. They don't.
Picture a set of Russian nesting dolls.
The largest doll — the one on the outside — is Artificial Intelligence. This is the broadest idea: any computer system that does something that would normally require human-level intelligence. Recognising faces. Translating language. Beating a human at chess. AI is the umbrella.
Open that doll and inside you find Machine Learning. This is AI that learns from data rather than following hand-written rules. Your email spam filter is machine learning. It wasn't programmed with a list of rules like "if the subject line contains 'FREE MONEY' then move to spam." It learned what spam looks like by studying millions of examples.
Open that doll and inside is Deep Learning. This is machine learning that uses neural networks — specifically, networks with many layers. The "deep" in deep learning refers to those layers. ChatGPT, image generators, voice assistants, real-time translation — all of it is deep learning.
All deep learning is machine learning. All machine learning is AI. But not all AI is deep learning, and not everything called AI actually learns anything.
Now you'll never mix them up again.
Why AI Gets Things Wrong: It's Not a Bug, It's the Design
You've seen it happen. AI confidently tells you a court case that doesn't exist. Invents a book that was never written. States a statistic with complete authority — completely fabricated.
This has a name: hallucination. And once you understand how AI works, you immediately understand why it happens.
Go back to the library student. They've read every book ever published. But they've never fact-checked any of it against reality. And when books contradict each other, they don't know which one is right. So they blend the two accounts together and produce something that sounds coherent.
AI doesn't know what it doesn't know. It predicts the most likely next word. Sometimes that produces accurate information. Sometimes it produces fluent, detailed, completely wrong information.
This isn't a bug waiting to be fixed in a future update. It's a fundamental property of how language models work. The model can't step outside itself and verify its own outputs against the real world. It doesn't have a "real world" to check against. It only has the patterns from training.
The fix isn't a smarter AI. The fix is a smarter user.
Always verify important claims from AI against primary sources. Treat it like a well-read friend who sometimes misremembers — not like an encyclopedia.
FAQ: Your Top Questions About How AI Works
Does AI actually understand what I say?
No. Not in the way you understand this sentence. When you read "it's raining outside," you picture rain, feel some version of disappointment or relief, maybe glance at the window. AI does none of that. It processes the statistical relationships between your words and generates a likely response. The result often looks like understanding. That's because it was trained on text written by people who understood things. But the model has never experienced anything. Understanding requires experience. AI has none.
Is AI the same as a search engine?
No — and this is an important difference. A search engine retrieves. When you Google something, it finds documents that already exist and ranks them for you. A language model generates. When you ask ChatGPT something, it constructs a brand new response, word by word, from scratch. That's why AI can write things that don't exist anywhere — and why it can confidently state things that are completely wrong. Google points you to sources. AI produces something new every time.
How does AI learn from new information?
Most AI models have a knowledge cutoff date. They were trained on data up to a specific point and they don't automatically absorb new information after that. To update what the model knows, engineers have to retrain or fine-tune it on new data — a process that takes significant time and computing resources. Some AI tools solve this by giving the model real-time internet access. But the core model isn't learning from your conversations as you have them.
What is the difference between how AI works and how a human brain works?
The human brain runs on about 20 watts of power — roughly the same as a dim light bulb — and simultaneously handles vision, hearing, memory, movement, emotion, and language. AI training can consume megawatts of electricity. Your brain built a language model from thousands of hours of real-world experience, including everything you saw, heard, and felt. AI learned from text alone. Your brain updates continuously, every waking moment. An AI model is frozen at its training cutoff. They produce some similar outputs but they're built completely differently.
Why does AI sometimes sound so confident when it's wrong?
Because confidence and accuracy aren't connected in a language model. The model isn't weighing evidence and deciding how certain to be. It's predicting words — and words like "in 1987, researchers discovered..." or "the study clearly showed..." are common patterns in training data. So the model produces them fluently, without any internal flag that says "actually, I'm making this up." This is the single most important thing to know about using AI responsibly: it has no idea when it's wrong.
The Bottom Line
Here's what you now know that most people don't.
AI is not thinking. It's predicting. It's a library student who has read everything and experienced nothing — generating the next word, and the next, and the next, based entirely on patterns it absorbed during training.
How AI works comes down to this: enormous amounts of data, billions of mathematical dials tuned by trial and error, and a single question asked millions of times per second — what word comes next?
The output looks like magic. The mechanism is entirely explainable. And now you can explain it.
That was a long one — hope it was worth the read.
Tell me: before you read this, what did you think AI was actually doing? Drop it in the comments. Some of the answers I've heard are genuinely surprising.
See you Saturday!
Vedant Helaskar · Decoding Tech One Bit at a Time · No Code. No Jargon. Just Tech That Clicks.
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