What Is an LLM, Really? A Gigantic Autocomplete
A Large Language Model, abbreviated LLM, is, put simply, a computer program that takes a piece of text and answers the question "what word should come next?" with surprising accuracy. ChatGPT, Google's Gemini, Anthropic's Claude and the rest are all LLMs. The "large" in the name comes from the fact that both the text it learns from and the model itself are genuinely enormous, and we'll return to both shortly.
My favorite analogy is the next-word suggestion on your phone's keyboard. When you type "The weather today is very...", the keyboard offers "nice," "hot" or "cold." At its core, an LLM does exactly this; but unlike that tiny model on your phone, it has read a large slice of the internet and practiced on billions of sentences. That is why it can produce not just one word but an entire paragraph, an essay, even working computer code, weaving them one piece at a time.
It feels counterintuitive at first that such a simple idea turns into something that can summarize a legal document, write poetry or spot a bug in code. The secret lies not in any single prediction, but in repeating that prediction billions of times, and in the model absorbing the patterns of language, reasoning and the world along the way. In the rest of this article we'll unpack, step by step, how such a small idea becomes such a large leap.
The Idea at the Core: Predicting the Next Word (Token)
LLMs have a single core skill: predicting the most likely unit that comes next in a piece of text. In technical terms this is called "next-token prediction." Here a "token" is the smallest piece the model uses while processing text. Often it corresponds to a word, but a long word like "autocompletion" may be split into several tokens. So a token is sometimes a whole word, sometimes a fragment of one, sometimes just a punctuation mark.
At each step the model produces a probability distribution over all possible tokens, in other words a numerical answer to the question "out of the thousands of words that could come next, how likely is each one?" After the phrase "The capital of Turkey is..." it assigns a very high probability to the token "Ankara" and a near-zero probability to "banana." It then picks a token according to those probabilities, appends it to the end of the text, and repeats the whole process from the top. Predict, append, predict again: this loop is the mechanism that weaves the fluent paragraphs you see, one word at a time.
The subtle part is that the model doesn't have to pick the single most likely token every time. It usually makes a random choice weighted by the probabilities; a setting called "temperature" controls the dose of that randomness. Low temperature makes the model more predictable and cautious, while high temperature makes it more creative but sometimes more rambling. This is the main reason asking the same question twice can give you two different answers.
Training on Massive Text: Where Does the Model Learn This?
A model knows that the word "capital" is usually followed by the name of a city, even though no one explicitly told it so. It figures this out on its own during a process called "training," by reading enormous amounts of text. Training data typically consists of web pages, books, encyclopedias, articles, forum discussions and code repositories; in total, on the order of hundreds of billions, even trillions, of words.
The mechanics of training are surprisingly simple. The model is shown a fragment of a sentence, the next word is hidden, and the model is asked to predict it. The model makes a guess, the guess is compared with the real word, and to the extent it was wrong, the numerical settings inside the model are nudged by a tiny amount. This procedure is repeated countless times over the massive text. No one teaches the model grammar rules, historical facts or logic; it captures all of these as patterns, as a byproduct, simply while trying to "predict the next word a little better."
After this base training, a second stage usually follows: fine-tuning with human feedback. Here humans rate the model's responses, rewarding the ones that are more helpful and safer. This process, called "RLHF" (reinforcement learning from human feedback), turns a raw word-predictor into a genuinely useful, polite, instruction-following assistant. So the personality and helpfulness of the model you chat with today are largely the product of this second stage; the first stage teaches it language, the second teaches it how to behave.
Parameters: The Billions of Tiny Knobs the Model "Knows"
Those "numerical settings" adjusted during training are called parameters. You can think of a parameter as a tiny knob, a dial on a gigantic audio mixing board. All of the model's knowledge, linguistic intuition and pattern-recognition ability is hidden in exactly where these dials sit. Training is essentially nothing more than the process of setting these billions of dials, one by one, to the right positions.
Modern large models typically have tens of billions, even hundreds of billions, of parameters. The big numbers you hear next to names like "GPT" or "Llama" often refer to precisely this parameter count. As a rough rule, more parameters mean more capacity to capture patterns and behave with more nuance; but the relationship is not linear. Beyond a certain point, the quality of the data, the training method and the model's architecture become far more decisive than raw parameter count.
It's worth correcting a common misconception here: parameters are not a database where the model stores the sentences it read one by one. The model does not hold its training text inside like a library; instead it "compresses" the statistical patterns in the text into these dials. Like someone who reads a book cover to cover, then closes it and can no longer see the pages but retains the gist. That is why an LLM often remembers information not word for word, but like a blurry summary. This distinction also lies at the root of the "making things up" (hallucination) problem we'll touch on shortly.
The Secret Behind Seeming to "Understand": Attention and the Transformer
If the model only predicts the next word, how does it manage to grasp a question and give a coherent, context-appropriate answer? The answer lies in the architecture underlying almost every LLM today, called the "Transformer," and the "attention" mechanism at its heart. Attention lets the model decide, while processing a given word, which of the other words in the text to weight and by how much; in effect, it keeps asking the model "what should I focus on right now?"
An example makes this concrete: in the sentence "I put the bag on the table because it was very heavy," what does the pronoun "it" refer to? A human mind instantly says "the bag." Thanks to attention, the model too forms a strong link to the word "bag" while processing "it," and a weak link to "table." Had you changed the sentence to "because it was very large," attention would most likely shift to "table" instead. This ability to capture the relationships between words in order to predict the next one correctly produces much of what looks, from the outside, like "understanding."
So does the model really understand? This is still philosophically contested, and the honest answer is "not in the sense a human does." The model has no consciousness, no intent, no lived experience of the world. What it does is apply, with extraordinary finesse, the patterns it learned from massive text. Yet those patterns are so rich that the resulting behavior becomes, in many practical situations, indistinguishable from understanding. This gray zone between "pretending to understand" and "understanding" is one of the most provocative and least resolvable corners of the AI debate.
The Context Window: The Model's Short-Term Memory
When you chat with an LLM, you notice it behaves as if it remembers what you just said. What makes this possible is a concept called the "context window." The context window is the amount of text the model can "see" and take into account at once, measured in tokens. You can think of it as the size of a desk in front of the model: it can consider everything that fits on that desk at once, and it cannot see anything that doesn't fit.
The fact that this window is limited has important consequences. If a conversation or document is longer than the context window, the oldest parts start falling off the desk; that is, beyond a certain point the model no longer sees a detail from the very start of the conversation. This is why, in very long chats, it's common for the model to act as if it has forgotten the initial instructions. The illusion that the model has a permanent memory often comes from here; in reality it is a temporary context, re-laid on the desk with each new message.
In recent years context windows have grown dramatically; today some models can take in hundreds of thousands, even millions, of tokens at once. This makes it possible for a model to read a long contract or an entire case file in one go and answer questions about it. Still, no matter how large the context window grows, the distinction between the permanent knowledge the model gained through training and the temporary information specific to the current conversation always holds. We clarify that critical distinction in the next section.
The Difference Between Training and Inference
One of the most commonly confused aspects of LLMs is the difference between the moment the model "learns" and the moment it is "used." Training is the process in which the model acquires knowledge by adjusting its parameters from massive text; it takes weeks, is extremely expensive, and is done only once (or rarely). You can compare it to the long years spent in school: intensive and costly, but a one-time period of accumulation.
Inference, on the other hand, is the moment you take the trained model and ask it a question. Here the model no longer learns anything new; its parameters are fixed, essentially frozen. The model simply uses those fixed dial positions to produce a response to your text. So when you ask ChatGPT something, the model does not "learn" from you; it merely applies what it learned earlier to your current context. It's like a graduated expert using their existing knowledge to solve a new case, without needing to re-earn their diploma.
This distinction has two practical consequences. First, a model's "knowledge" freezes at the date the training data ends; this is called the training cutoff, and it is precisely why the model does not, on its own, know about events after that date. Second, the things you think you've "taught" the model in a conversation are limited to that conversation's context window; when you close the chat, the model forgets them, because nothing was written to its permanent parameters. The way to keep a model up to date and domain-specific usually runs not through expensive retraining, but through the techniques we cover in the next section.
What Can It Do, What Can't It? Hallucination and Limits
LLMs are extraordinarily good at summarizing, rewriting, translating, classifying, generating ideas and linguistic tasks in general. They can explain a topic at different levels, change the tone of a text, write code, or simplify a dense paragraph. Where they are strong is in tasks dominated by language and patterns, where a single exact correct answer isn't required.
Their best-known weakness, by contrast, is the phenomenon called "hallucination": the model producing information that sounds perfectly confident and fluent but is actually wrong, or even entirely made up. The reason, as we saw earlier, is that the model is not pulling facts from a database but generating what "looks statistically likely." Citing a court ruling that doesn't exist or giving a wrong date stems not from ill intent but from its nature, which optimizes for probability rather than truth. This is exactly why, in sensitive domains, an LLM's output must always be independently verified.
Models can also be unaware of recent events (because of the training cutoff), stumble on complex multi-step reasoning or exact arithmetic, and reflect biases in the training data. To get past these limits, two powerful methods were developed: "tool use" (the model calls a calculator or a search engine) and especially "RAG" (retrieval-augmented generation). Before letting the model generate an answer, RAG fetches relevant documents from a reliable, up-to-date source and places them in the context window; so the model answers based on the real text in front of it rather than inventing from memory.
At EcoFluxion we live these distinctions in practice every day. MevzuatBot, the engine of our legal-tech product İçtiHub, operates precisely in a domain where hallucination is unacceptable: a wrong article or a fabricated precedent can lead to a real legal consequence. That is why we ground answers not in the model's memory but in the actual legislation and case-law texts retrieved through RAG. In the end, what makes an LLM both powerful and trustworthy is often not the model itself, but the engineering carefully built around it.