Large Language Models Explained: How They Actually Generate Text
How large language models like ChatGPT and Claude actually work, what 'parameters' really means, and why they predict text rather than truly understanding it.
Large language models sit behind nearly every consumer AI tool people interact with today, from chatbots to writing assistants to AI features built into search engines. Despite how naturally they seem to converse, understanding what’s actually happening underneath, statistical prediction rather than genuine comprehension, changes how a person should reasonably trust and use their output.
The Core Idea: Predicting the Next Word
At its most fundamental level, a large language model, or LLM, is trained to predict the most probable next word, or more precisely the next token, given everything that came before it in a piece of text. By repeating this prediction process one token at a time, feeding each new token back in as additional context, an LLM can generate entire coherent paragraphs, answer questions, summarize documents, or write code. This might sound like a simple trick, but at sufficient scale, trained on enormous quantities of text, this prediction process produces output that captures grammar, factual patterns, reasoning-like structure, and stylistic nuance well enough to be genuinely useful across an enormous range of tasks.
What "Parameters" Actually Measures
A model’s parameter count, often cited as a headline figure like “70 billion parameters,” refers to the number of adjustable internal values the model uses to make its predictions. These parameters are set during training and collectively encode the patterns the model learned from its training data. Generally, more parameters allow a model to capture more nuanced patterns and handle a wider range of tasks, but parameter count alone doesn’t guarantee better performance, since training data quality, training technique, and architecture design all meaningfully affect how well a model actually performs, sometimes allowing a smaller, well-trained model to outperform a larger, less carefully trained one on specific tasks.
Training on Text Doesn't Mean Understanding Text
A crucial distinction that gets lost in casual conversation about LLMs is that these models don’t understand language the way humans do, with grounded knowledge of the physical world, genuine reasoning, or awareness of truth versus falsehood. They learn statistical relationships between words and concepts as they appear together across their training data. This is why LLMs can produce fluent, confident-sounding text that is nonetheless factually wrong, a well-documented limitation often called hallucination, and why their apparent reasoning ability, while genuinely useful, doesn’t reflect the same kind of understanding a human expert would bring to the same problem.
Why Context Window Size Matters
An LLM can only consider a limited amount of preceding text, called its context window, when generating each new piece of output. A larger context window lets a model reference more of a long document, a longer conversation history, or more code in a single request, which has become one of the most actively competitive areas of improvement between different LLM providers, since a larger context window generally makes a model more useful for tasks like analyzing lengthy documents or maintaining consistency across long conversations.
Why Different LLMs Behave Differently
Beyond raw scale, the specific data a model was trained on, the fine-tuning process used to align it toward being helpful and safe, and the specific techniques used during training all shape a given LLM’s particular strengths, tone, and blind spots. This is why different LLMs, even ones with roughly comparable parameter counts, can feel meaningfully different in tone, accuracy on specific topics, and reliability for particular tasks like coding versus creative writing.
Bottom Line
Large language models generate text by predicting likely sequences of tokens based on patterns learned from enormous amounts of training data, not through genuine understanding of the world. That distinction explains both their remarkable usefulness across a huge range of tasks and their well-documented tendency to occasionally produce confident, fluent, but incorrect output, which is why verifying important factual claims from an LLM against a reliable source remains good practice regardless of how convincing the response sounds.
Sources
- Academic and industry research papers on transformer-based language model architecture
- OpenAI, Anthropic and Google DeepMind technical documentation and model cards
- Independent AI research publications on language model capabilities and limitations
- Stanford HAI, AI Index research on large language model development
