An LLM turns text into tokens, processes those tokens as numerical representations, and predicts what token is likely to come next. Follow “The cat sat on the mat.” through that process and you can see how tokenization, embeddings, attention, and decoding fit together—and why fluent output is not a guarantee of truth.
What happens when an LLM receives a sentence?
Suppose you enter “The cat sat on the mat.” The model does not handle it as a row of words in the way a person reads a sentence. Its tokenizer converts the text into a sequence of token IDs, which are the model’s numerical labels for pieces of text. It then transforms those IDs into representations that the neural network can process.
The model uses the resulting sequence and the surrounding context to calculate scores for possible next tokens. If it is generating a response, a decoding method uses those scores to choose a token; the model appends that token to the context and calculates again. Repeating this loop produces text a token at a time.
One sentence, several stages
- Tokenize: split the input into model-specific text units and map them to token IDs.
- Embed and position: turn each ID into a vector and add information that lets the model represent the token’s position in the sequence.
- Process context: use Transformer blocks to update the representations, including through self-attention and feed-forward layers.
- Score possible continuations: use an output head to produce a score for each candidate next token.
- Select and repeat: convert scores into probabilities, select a token according to the decoding policy, and run the process again until a stopping condition is reached.
How does tokenization turn words into model input?
A tokenizer’s units are not necessarily whole words. Depending on the model, a token can represent a word, part of a word, punctuation, or another text fragment. The sentence might therefore become a sequence containing several pieces for a word, but its exact segmentation depends on the tokenizer. There is no single universal token sequence for “The cat sat on the mat.”
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Common subword tokenization approaches include Byte Pair Encoding (BPE), Unigram, and WordPiece. Using subword pieces keeps a model’s vocabulary manageable while allowing less common words to be represented as combinations of pieces the vocabulary already contains. Token IDs are bookkeeping labels—not meanings or word counts. A model’s internal computation uses learned numerical representations associated with those IDs.
What are embeddings and positional information?
An embedding maps each token ID to a vector: a list of numbers used as the token’s initial representation inside the model. During training, the model learns parameters that shape these representations and the later transformations applied to them. An embedding is not a dictionary definition. What a token contributes to the model’s computation depends on its learned representation and on how the surrounding context changes that representation.
Order matters: “the cat sat” and “the mat sat” contain overlapping pieces but express different relationships. Positional information gives the network a way to distinguish where tokens occur in the sequence. Without information about order, the model would have a much harder time representing how a token relates to what comes before or after it.
How does attention use the surrounding context?
A Transformer processes token representations through stacked blocks. In a self-attention operation, each position can weigh information from relevant positions in its context and use it to update its representation. For the example sentence, a position associated with “sat” can incorporate information from other tokens rather than treating every token as an isolated item. The model repeats attention and other transformations across multiple layers, building more contextual representations as the sequence moves through the network.
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →The Transformer architecture was introduced in the 2017 paper Attention Is All You Need, whose authors described a network based on attention mechanisms rather than recurrence or convolution. In practical terms, attention is a mechanism for combining contextual information; it is not evidence that the model consciously focuses on a sentence as a person would.
One theoretical account of self-attention describes a two-part pattern: hard retrieval of high-priority context tokens, followed by soft composition from those tokens. This is a way to analyze a mechanism, not a claim that every model literally performs a separate, human-like search for the most important words.
How does the model predict the next token?
After the Transformer blocks process the current context, an output head produces a score—often called a logit—for each token the model could emit next. A decoder turns those scores into a probability distribution. The model then selects a token according to a decoding policy, appends it to the sequence, and evaluates the expanded context.
If the input is only “The cat sat on the mat.”, the model is not necessarily being asked to continue the sentence. In a chat or writing task, the prompt usually also includes an instruction or question that guides what kind of continuation is wanted. The sentence is context; the output depends on the entire prompt and the model’s learned parameters.
Decoding choices change the output
- Greedy decoding chooses the highest-probability token at each step. It is direct, but repeated local choices can produce predictable or awkward text.
- Sampling chooses from a probability distribution, allowing variation. Temperature adjusts how sharply or broadly the probabilities influence that choice; top-p sampling limits the candidate set to tokens whose cumulative probability reaches a chosen threshold.
- Stopping rules determine when generation ends—for example, when the model emits a designated end token or reaches a configured limit.
These are generation controls, not changes to the model’s underlying training. Different policies can produce different continuations from the same prompt.
How does training differ from generating a response?
During pretraining, the model sees many text examples converted into token sequences. It makes predictions against training targets—commonly subsequent tokens for a generative model, though training objectives can also involve predicting masked tokens. A loss measures how far the predictions are from the targets. Gradient-based learning uses that loss to adjust the model’s parameters over repeated examples.
Inference is the generation phase: the trained parameters are held fixed while the model processes a prompt, calculates next-token probabilities, selects a token, and repeats. Training changes the model; inference uses the model. Neither phase requires the model to retrieve a complete answer from a database. A separate retrieval system can be added to supply documents, but absent such a system, the model generates from its learned parameters and current context.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What does “large” mean, and why does scale matter?
“Large” can refer to several related resources: the number of learned parameters, the volume of training data, and the computation used to train the model. These dimensions interact; parameter count alone does not describe all of a model’s capabilities or costs.
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OpenAI reported in 2020 that cross-entropy loss followed power-law scaling trends with model size, dataset size, and compute, across more than seven orders of magnitude. That result describes how a training objective changed with scale under the studied conditions. It does not establish that increasing scale by itself guarantees accurate facts, sound reasoning, or reliable behavior.
As a dated illustration of scale rather than a description of today’s typical models, Google’s 2022 technical post described LaMDA training on a corpus of 1.56 trillion words and a model family with sizes up to 137 billion parameters. Those figures belong to that reported system and period; they should not be read as universal thresholds for an LLM.
Does an LLM understand or look up what it says?
An LLM produces text by applying learned statistical patterns to token representations and context. Those patterns can support useful language behavior, but fluent wording alone does not show that a statement is true or that the model understands it in the human sense. A confident answer can still be wrong.
Unless a system explicitly includes an external retrieval component, an LLM is not looking up a complete answer as it generates each response. It predicts a continuation from its parameters and the prompt. For consequential information, check claims against reliable sources rather than treating fluency as verification.
What the one-sentence journey leaves out
The example captures the basic loop, but real systems add details: tokenizers differ, architectures use different training objectives, and context limits and efficiency techniques affect how much text can be processed. Many chat systems also wrap a language model in additional software that formats prompts, applies safety rules, or retrieves information. Those additions can change the overall experience without changing the core idea of generating through successive token predictions.
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