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An LLM works by repeatedly predicting the next token in a sequence. Your prompt is first split into model-specific tokens, converted to numerical IDs, and placed in the context the model can read. The trained network scores possible next tokens, a decoding rule selects one, and that token is appended to the context. The cycle continues until the model stops, and the resulting tokens are converted back into text.
That mechanism explains both the fluency and the limits of LLMs: they generate likely continuations from learned parameters. They do not automatically verify that a continuation is true, and answering a prompt does not normally update the model’s parameters.
The complete prompt-to-prediction pipeline
Inference (the act of answering) can be understood as a loop with six stages. The labels are useful across many systems, although the exact message format, network architecture, and serving optimizations vary by model.
| Stage | What happens | Immediate result |
|---|---|---|
| 1. Tokenization | Text is split into vocabulary units and each unit receives an integer ID. | A sequence of input token IDs |
| 2. Context assembly | The serving system supplies the prompt and any conversation material it has chosen to include. | The context presented to the model |
| 3. Model computation | The trained network uses the context to score possible continuations. | A probability distribution over candidate next tokens |
| 4. Decoding | A selection rule chooses one candidate, either predictably or with controlled randomness. | One newly selected token |
| 5. Iteration | The selected token is added to the context and the model predicts again. | A growing output-token sequence |
| 6. Detokenization | The output sequence is converted from token units into displayable text. | The response shown to the user, often streamed in pieces |
The model does not produce an entire paragraph in one indivisible operation. It produces one token at a time, while each new token can influence what comes next.
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1. Your words become tokens
What a token is
A tokenizer maps text to the units in a particular model’s vocabulary. A token can represent a character, part of a word, a complete short word, whitespace or punctuation. Common words may fit into one unit, while an unusual name, a long technical term, or text in another language may be split into several units.
Token boundaries are not universal. Two models can tokenize the same sentence differently because they use different vocabularies or encodings. Boundaries can also depend on surrounding characters. This is why a token count is not the same thing as a word count.
Why token counts affect your request
Models impose a maximum context length measured in tokens. That budget can include the prompt, conversation history, instructions supplied by an application, and the generated answer. If the combined sequence exceeds the model’s limit, a system may reject the request, omit older material, or truncate content according to its own policy.
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OpenAI gives rough English rules of thumb of about four characters or three-quarters of a word per token. Those are estimates, not a conversion formula. Code, tables, source text, emojis, and languages with different writing systems can produce very different ratios. Use the tokenizer for the model you actually call when an exact count matters.
2. The token IDs enter a context
After tokenization, the text is represented as integers that index the model’s vocabulary. A conversational application may combine your latest message with earlier turns and other instructions before sending the resulting sequence to the model. The model itself receives the token sequence; the product or API decides how messages are packaged and which conversation material is included.
This distinction matters when an answer seems to “remember” something. The apparent memory may simply be earlier text included in the current context. A new request that does not contain that text gives the model no direct access to it unless the application retrieves and inserts it or the model has a separate, explicitly documented memory mechanism.
3. The network scores possible next tokens
Prediction is a distribution, not a single word
For the current context, the model assigns scores to many possible next tokens. After those scores are converted into a probability distribution, high-probability candidates are generally more compatible with the preceding text, but lower-probability candidates can still be selected when sampling is enabled.
At the end of a sentence such as “The capital of France is,” a token corresponding to “Paris” may receive a high score. The distribution also contains alternatives, including punctuation or an incorrect continuation. The model is estimating what text is likely to follow the context; it is not consulting a built-in truth meter.
How context is used
Transformer-style language models use layers of computation that let representations of tokens interact. Self-attention is one mechanism for relating positions in the sequence, so the prediction can depend on words earlier in the context rather than only on the immediately preceding token.
It is inaccurate to picture attention as every word receiving equal focus. Theoretical work on next-token prediction studies how self-attention behaves under particular assumptions; a deployed model’s internal patterns are not a universal, human-readable map of its reasoning. The useful operational fact is simpler: the network transforms the supplied context into scores for the next token.
4. Decoding turns scores into a choice
The probability distribution still has to be turned into an actual token. Decoding settings determine how much of the distribution is eligible and how selection is performed. The settings affect the choice process; they do not retrain the network or change its learned parameters.
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| Decoding control | Effect in a typical implementation | Important qualification |
|---|---|---|
| Greedy or deterministic selection | Chooses the highest-scoring eligible token, making the same context tend to produce the same result. | Exact repeatability can still depend on the serving system and other settings. |
| Temperature | Changes how sharply or broadly probabilities are treated before sampling; lower values usually favor the top candidates. | The meaning and accepted range are implementation-dependent. |
| Top-k | Restricts sampling to the k highest-scoring candidates. | A larger k permits more alternatives; availability varies by model and API. |
| Top-p (nucleus sampling) | Chooses from the smallest group of candidates whose cumulative probability reaches a chosen threshold. | The candidate set changes with each distribution, so its size is not fixed. |
Google’s developer guidance documents top-k, top-p, and temperature as common controls and also cautions that changing them is not appropriate for some Gemini 3.x models. That warning illustrates a broader rule: do not assume a parameter has the same effect, name, or recommended value across providers.
5. The selected token is fed back into the loop
- Start with the current context. The model scores the next-token distribution.
- Apply the configured decoding rule. One token is selected.
- Append the token. It becomes part of the context for the next prediction.
- Check stopping conditions. Generation may stop at an end-of-sequence token, a configured stop sequence, a maximum output length, or a serving-side interruption.
- Repeat. If no stopping condition has fired, the model predicts another token.
Applications often display output incrementally. The underlying sequence is still generated token by token, but the serving layer can send newly decoded pieces to the interface as they become available. Streaming changes when you see the text, not the basic prediction loop.
Training and answering are different operations
The model’s ability to generate text comes from an earlier training process. During training, examples are used with a prediction objective: the network produces a guess for a token, an error is calculated against the training target, and an optimization procedure adjusts many parameters. Repeating this over large collections of examples changes the parameters so that useful statistical regularities are represented.
Inference uses those already-trained parameters on a particular prompt. The ordinary act of asking a question does not, by itself, update the parameters or teach the model a permanent new fact. A system can separately store conversation data, retrieve documents, or run a later fine-tuning process, but those are additional mechanisms rather than consequences of one normal prompt.
| Aspect | Training | Inference |
|---|---|---|
| Input | Many examples selected by the training process | A particular prompt and whatever context the application supplies |
| Purpose | Adjust model parameters using prediction errors | Apply fixed trained parameters to generate a response |
| Output of the step | Updated parameter values over optimization steps | Scores and selected output tokens |
| Does an ordinary user prompt permanently retrain the model? | Training is a separate process | No, not merely by generating an answer |
Some systems perform additional post-training, instruction tuning, or preference optimization before deployment. Those stages can shape how a model follows requests, but they remain distinct from the token-by-token inference loop described above.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why a likely continuation can still be wrong
Next-token prediction rewards a continuation that fits learned patterns, not a statement that has been checked against a current database or a source document. A sentence can be grammatically polished and highly probable while containing a false date, invented citation, or outdated fact.
Truth checking requires another step, such as consulting authoritative material, using a retrieval system that supplies relevant documents, or having a person verify the claim. The generation mechanism alone provides no guarantee that the most probable continuation is factually correct.
What changes from one LLM to another
Token vocabulary, token boundaries, maximum context length, network design, training data, parameter values, post-training methods, safety controls, and decoding interfaces can all differ. Consequently, a token count or sampling setting that works for one model may not transfer directly to another.
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A small worked example
Imagine the prompt “A watched pot never”. The tokenizer first converts the characters into that model’s token IDs; the result might contain a whole word for some pieces and multiple units for others. The network processes the sequence and assigns probabilities to possible next tokens. “Boils” may be among the strongest candidates, while punctuation or an unrelated word receives lower scores.
With deterministic selection, the highest-scoring eligible token is chosen. With sampling, the system may filter the candidate list and then draw according to the adjusted probabilities. The chosen token is appended, so the next prediction is made for “A watched pot never boils” rather than for the original prompt. The loop continues until a stop condition is reached, and the final token sequence is decoded into the displayed sentence.
Common misconceptions to avoid
- “The model predicts a whole answer at once.” It generates a sequence through repeated next-token decisions.
- “One token always equals one word.” Tokens can be smaller or larger than words, and the mapping varies by model and language.
- “Higher temperature adds knowledge.” Temperature changes selection behavior; it does not add training data or alter parameters.
- “The prompt permanently teaches the model.” Normal inference applies existing parameters. Persistent learning requires a separately implemented process.
- “A fluent answer has been verified.” Plausibility and factual validation are different tasks.
- “Attention simply shows what the model is thinking.” Attention patterns are computational mechanisms, not a universal, directly readable explanation of reasoning.
The practical mental model
When diagnosing an LLM response, ask four questions in order: What tokens did the system actually receive? Which earlier material was included in context? Which decoding rule selected each continuation? Was the resulting text independently checked for truth? Those questions separate input handling, model computation, randomness, and verification—four issues that are often blurred together when people say an LLM “understands” a prompt.
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