How Large Language Models Work: A Deep Dive

Every chatbot you have ever argued with — ChatGPT, Claude, Gemini, DeepSeek — is doing one strange, simple thing: predicting the next token, again and again, at enormous speed. The essays, the bug fixes, the awkward poems all fall out of that single trick. Once you see the machinery underneath, you also see why these systems fail the way they do.

Words become tokens

Models cannot read; they compute. Your text is first chopped into tokens — chunks that are often words or pieces of words. "Unbelievable" may split into pieces, and Urdu or code-mixed Roman Urdu tends to shatter into many more tokens than English, which is one reason local-language prompts cost more and stumble more. Each token becomes a list of numbers, and from that point the model is doing arithmetic on numbers, nothing else.

Attention: the 2017 idea that changed everything

Older language models read text one word at a time and kept forgetting the start of long sentences. The 2017 research paper "Attention Is All You Need" introduced the transformer, which reads everything at once and lets each word weigh the importance of every other word. In "the cat sat on the mat because it was tired", understanding "it" means attending strongly to "cat". That weighting mechanism is called attention, and because transformers process the whole sequence in parallel, they could be scaled up on GPU clusters — and scale, it turned out, was nearly everything.

Training happens in three passes

Pre-training comes first: the model reads an enormous slice of the internet with one job, guess the next token, and its billions of internal weights get nudged every time it guesses wrong. Out of that dumb repetition comes grammar, facts, and a startling amount of reasoning ability. Then supervised fine-tuning teaches it to behave like an assistant, using human-written examples of good answers. Finally comes alignment: reviewers rank different responses, and the model is nudged toward the answers people prefer — RLHF, the step that made chatbots polite instead of merely fluent.

The context window

A model's working memory is its context window: everything it can hold in mind at once. Early chatbots managed a few thousand tokens, roughly a long email thread. Frontier models now claim windows in the millions of tokens — whole codebases, whole books — though attention dilutes as the window grows, and the marketing claim and the everyday usefulness are not always the same thing. When a chatbot forgets the start of your conversation, you have met the window's edge.

Why they hallucinate

Here is the uncomfortable truth: nothing in that pipeline checks facts. The model's only obligation is to produce plausible next tokens. When the truthful continuation is not in its weights, it still produces the most statistically likely one — fluent, confident, and sometimes invented. This is not a bug waiting to be patched; it is the mechanism. The defences are practical. Ask for sources and actually open them. Keep important facts out of the model and in your own documents — the retrieval pattern businesses use. And never let confident prose substitute for verification.

A model fills the gaps in its knowledge with plausible guesses. Gaza's reality is the opposite of a guess — it is being documented by the people living it, at terrible cost. Believing witnesses over fluent noise remains a human responsibility no tool will take over for us.

One more thing about prediction: we do it professionally. Our team at HTG Travels predicts the next problem before it lands — the cancelled flight at 2 a.m., the missed connection, the visa snag mid-trip. Save our WhatsApp number before you fly; being rebooked in minutes beats an hour of chatbot loops at a deserted airport counter.