How Do Large Language Models Work? A Plain-Language Explanation

If someone says "The capital of Kenya is ___," you know the answer is "Nairobi." You know this because you have seen the pattern before, in school, in books, in conversation. An LLM works the same way, but at massive scale.

During training, the model sees trillions of tokens (word pieces) from books, websites, code, and other text. For each position in the text, it learns to predict what token comes next. Over billions of examples, it builds a statistical map of language: what follows what, in what context.

When you send a prompt, the model reads your tokens and predicts the most likely next token. It appends that token to the sequence, then predicts the next one, and so on, until it decides to stop. Your entire conversation is just a long sequence of tokens being extended.

Training is the expensive, one-time phase where the model learns. A company like OpenAI or Anthropic feeds enormous datasets through the model, adjusting billions of internal parameters (called weights) until the model becomes good at prediction. Training GPT-4 class models reportedly cost tens of millions of dollars in compute. This is not something you do at home.

Inference is the cheap, repeated phase where you use the model. When you call the OpenAI API or chat with Claude, the pre-trained model runs your input through its weights and generates output. This costs fractions of a cent per request for most use cases.

An analogy: training is like a chef spending years learning to cook. Inference is the chef making you a meal. The years of learning happened once. Each meal is fast and cheap by comparison.

LLMs do not read words the way you do. They break text into tokens, which are word pieces. Common words like "the" or "is" are single tokens. Uncommon words get split: "embeddings" might become ["embed", "dings"]. Numbers often get split digit by digit.

This matters for two practical reasons:

• Pricing. API costs are per token, not per word. A sentence with long or unusual words costs more tokens than a simple one.
• Context limits. Models have a maximum number of tokens they can process at once (the context window). If your prompt plus the expected response exceeds that limit, the model cannot handle it in one call.

As a rough guide, one token is about three-quarters of an English word. A 1,000-word article is roughly 1,300 to 1,400 tokens.

You do not need to understand the internals to use LLMs, but a rough picture helps you reason about their behavior.

The transformer architecture has two key components:

• Attention. For each token being generated, the model looks back at every previous token and decides which ones are most relevant. When generating the word "Nairobi" in "The capital of Kenya is Nairobi," the model pays strong attention to "capital" and "Kenya." This attention mechanism is what allows the model to handle long-range dependencies, connecting a pronoun to a noun mentioned paragraphs ago.
• Feed-forward layers. After attending to relevant tokens, the model passes the information through layers of mathematical transformations that refine the prediction. These layers are where the "knowledge" lives, encoded as billions of numerical weights.

The model stacks many of these attention + feed-forward blocks (GPT-4 reportedly has over a hundred layers). Each layer refines the representation, from raw word patterns in early layers to abstract concepts in later ones.

Since the model generates output by picking the most probable next token, it will always produce something. If it does not have enough information to give the right answer, it does not say "I don't know" by default. It picks the next most probable token anyway, which might be plausible but wrong.

This is called hallucination. The model is not lying. It is doing exactly what it was trained to do: generate probable text. The problem is that probable text is not always true text.

Hallucinations are more likely when:

• You ask about niche or recent topics that were underrepresented in training data
• You ask for specific numbers, dates, or URLs
• You ask the model to reason about something that requires real-world verification

RAG (retrieving relevant documents and injecting them into the prompt) is the most practical way to reduce hallucinations in production apps, because it gives the model evidence to base its answer on.

Understanding how LLMs work changes how you build with them:

• The model does not remember between API calls. Each request is independent. If you want conversation history, you send the full history as context in every request.
• Order matters. The model processes tokens left to right. Information placed early in the prompt influences everything that follows. Put important instructions at the beginning.
• More context is not always better. Stuffing irrelevant information into the prompt can confuse the model. Retrieve only the relevant chunks.
• The model is not deterministic by default. Temperature and sampling settings add randomness. The same prompt can produce different outputs on different runs. For reproducible behavior, set temperature to 0.
• The model does not "know" facts the way you do. It has seen statistical patterns. For anything that matters (medical, legal, financial), verify the output against a trusted source.