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An LLM turns your text into tokens, processes those tokens as context, assigns scores to possible next tokens, and uses a decoding rule to choose one. It appends the choice to the context and repeats the cycle until it reaches a stopping condition, then converts the token sequence back into readable text. The model learned its parameters earlier during training; it is applying those fixed, trained parameters during your request.
The complete prompt-to-answer pipeline
A useful way to understand a large language model (LLM) is to follow one request through seven stages. The details differ by model and product, but the core inference loop is consistent: represent text as tokens, compute a probability distribution for the next token, select one token, and run the loop again.
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| Stage | What enters | What happens | What leaves |
|---|---|---|---|
| 1. Tokenization | Your characters and words | A model-specific tokenizer splits text into vocabulary units and maps them to integer IDs. | A sequence of token IDs |
| 2. Context assembly | Prompt, and possibly conversation or system content | The serving application packages the available messages and enforces the model’s context limit. | The input sequence used for this request |
| 3. Model computation | Input token IDs and trained parameters | Neural-network layers use the sequence as context and calculate scores for possible continuations. | Scores (logits) for the next token |
| 4. Decoding | Next-token scores and decoding settings | A deterministic or stochastic rule filters and selects one candidate. | One chosen token ID |
| 5. Context update | The chosen token | The token is appended to the sequence. | A longer context for the next step |
| 6. Stopping check | Newly generated sequence | The system checks for an end token, a length limit, or an application stop condition. | Continue or stop |
| 7. Detokenization | Output token IDs | The tokenizer converts units back into text, often incrementally for streaming. | Displayed response text |
This is autoregressive generation: each new token is conditioned on the prompt plus every token generated before it. It is not a single lookup that retrieves a finished paragraph.
1. Your prompt is converted into tokens
Before an LLM can process text, a tokenizer divides it into the model’s vocabulary units. A unit may be a character, part of a word, a complete common word, punctuation, whitespace pattern, or another learned fragment. The tokenizer then replaces each unit with an integer ID. OpenAI describes the inference process as translating a textual prompt into “a sequence of input tokens—integers that index into the model’s vocabulary.”
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Tokens are not words
Token count and word count are different measurements. Common words may occupy one token, while an uncommon name, a long technical term, code, an emoji, or text in another language may occupy several. Even the same character sequence can receive different boundaries under another model or encoding. OpenAI gives rough English rules of thumb of about four characters or three-quarters of a word per token, but labels those figures as estimates rather than a conversion formula.
Why token boundaries matter
Tokenization affects how much text fits in a context window, how much output can be generated, and the cost of services that bill by tokens. It also explains some odd behavior: a model may handle a familiar word easily but struggle with an unusual string whose pieces are poorly represented. Punctuation and spaces are part of the tokenized sequence, so changing formatting can change the IDs the model receives.
2. The application builds the context
The raw prompt is not always the entire input. In a chat product, the serving system may include earlier turns, a system instruction, tool results, or other metadata. The model receives whatever token sequence that product or API has assembled. Exact message wrappers and hidden fields are implementation-specific, so two interfaces can send different contexts even when the visible user message is identical.
Context limits are model-specific
Every model has a maximum context length measured in tokens. The prompt, any included conversation history, and the generated answer all consume that budget according to the product’s rules. If the available context is exceeded, an interface may reject the request, truncate older content, or summarize it. You should therefore treat “the conversation” shown on screen and “the tokens actually sent” as related but not guaranteed to be identical.
Order changes meaning
Tokens are processed as an ordered sequence. Moving an instruction, example, or constraint can change the continuation even when the same words remain present. A serving system can also place higher-priority instructions before your message. This is why a prompt’s structure—headings, delimiters, examples, and explicit output requirements—can matter without changing the underlying model weights.
3. The model scores possible next tokens
With the context represented as token IDs, the trained neural network computes a score for each token in its vocabulary as a possible next item. After normalization, these scores are treated as a probability distribution. The highest-scoring token is the model’s most likely continuation under that context, but it is not automatically selected in every configuration.
What “attention” means here
Transformer-style models use mechanisms commonly called self-attention to combine information from positions in the sequence. Attention allows the computation at a position to use other positions, subject to the model’s architecture and masking rules. It is not accurate to say that attention looks at every word equally, or that an attention map is a complete, human-readable explanation of reasoning. The theoretical work by Li, Huang, Ildiz, Rawat, and Oymak analyzes next-token prediction with self-attention under specified conditions; it does not provide a universal picture of every deployed model.
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The distribution represents learned patterns for continuing text. A high probability means that a token is favored by the model’s parameters and the supplied context, not that an external source has verified the statement. Unless a system explicitly retrieves and checks information, generation and fact-checking are separate operations.
4. Decoding chooses one token
The model’s scores must be turned into one concrete token before generation can proceed. A decoder can choose the highest-probability candidate every time, or sample among candidates. Settings are model- and implementation-dependent, so the same names do not guarantee identical behavior across products.
| Setting or method | Effect | Practical implication |
|---|---|---|
| Greedy or otherwise deterministic selection | Chooses the current highest-scoring eligible token. | Usually repeatable for the same context and configuration, though surrounding systems can still differ. |
| Temperature | Reshapes the relative sharpness of the probability distribution before sampling. | Lower values favor the strongest candidates; higher values permit more variation. The useful range and behavior depend on the model. |
| Top-k | Restricts sampling to the k highest-scoring candidates. | Removes the long tail of unlikely tokens while retaining a fixed number of options. |
| Top-p (nucleus sampling) | Restricts sampling to the smallest set whose cumulative probability reaches p. | The number of eligible tokens changes with the distribution: a confident step may have a small set, while an uncertain step may have a larger one. |
Google’s developer guidance documents top-k, top-p, and temperature, and cautions that changing these controls is not appropriate for some Gemini 3.x models. Follow the current documentation for the specific model rather than assuming a universal recipe. Decoding changes token selection; it does not retrain the model or alter its learned parameters.
5. The chosen token is fed back into the loop
After decoding selects a token, that token is appended to the context. The model then calculates a new distribution conditioned on the expanded sequence. A single generated word can therefore affect every subsequent choice. This feedback loop continues one token at a time, not one sentence at a time.
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Generation ends when the model emits an end-of-sequence token, reaches a configured output limit, encounters an application-defined stop sequence, or is interrupted by the service. The system then has a sequence of output token IDs. Streaming interfaces detokenize and display pieces as they become available, while non-streaming interfaces wait and return the completed text.
Training and inference are different jobs
Training happens before you submit a prompt. During training, the model sees many examples and adjusts numerical parameters to reduce prediction error on a learning objective, commonly next-token prediction for language models. Repeated updates encode statistical regularities in those parameters.
Inference is the later act of applying the trained parameters to a particular context. An ordinary prompt does not normally update the model’s parameters or teach it permanently. It can influence the current answer because its tokens are present in the context, and a product may separately store conversation history or user preferences, but that storage is not the same as changing the base model through each question.
| Question | Training | Inference |
|---|---|---|
| When does it occur? | During model development or a later fine-tuning process. | Each time the service answers a request. |
| What changes? | Model parameters are adjusted using examples and an objective. | Parameters are normally fixed; the active context changes. |
| What is the input? | Large collections of training examples and optimization settings. | A prompt and any context the serving system includes. |
| What is the output? | An updated set of parameters. | A sequence of selected output tokens. |
Why a fluent answer can still be wrong
The mechanism optimizes for a plausible continuation, not a guaranteed true one. A response can be grammatical, detailed, and confidently phrased while containing an invented citation, a mistaken date, or a calculation error. The probability distribution reflects what the model has learned about text and the current context; it is not, by itself, a live verification step.
Signals that deserve checking
- Claims with precise numbers, dates, quotations, legal requirements, or medical consequences.
- Statements about events after the model’s knowledge cutoff or information that changes frequently.
- References that cannot be opened or do not contain the quoted material.
- Answers produced from an ambiguous prompt where several interpretations are possible.
Use retrieval, a calculator, executable tests, or a primary source when accuracy matters. Those tools can supply evidence to the context, but the LLM still generates the final wording through the same token-by-token process.
How to write prompts that work with the mechanism
- State the task and success condition. Say what must be produced and how you will judge it, such as “return a three-column table” or “show each algebra step.”
- Supply the relevant context. Include definitions, data, audience, date, and constraints instead of assuming the model can infer them.
- Separate instructions from material. Labels, headings, and delimiters make it clearer which tokens are commands and which are text to transform.
- Give a small example when format matters. An input-output example provides a pattern the next-token predictor can continue.
- Ask for uncertainty explicitly. Require the model to identify assumptions, missing information, or claims that need verification.
- Keep the active context focused. Irrelevant history consumes tokens and introduces competing patterns, while a context limit may force older material out.
- Verify consequential output. Treat a polished completion as a draft until independent evidence supports it.
Common misconceptions, corrected
“The model searches the web for every answer.”
Not inherently. The core inference loop calculates continuations from the supplied context and trained parameters. A product can add web retrieval or tools, but that is an additional system around the model.
“The model understands a whole paragraph at once and writes the answer in one step.”
The model processes the available sequence to score the next token, then repeats after appending the selection. The visible paragraph is the result of many such steps.
“Randomness means the model is learning.”
Sampling can make two answers differ without changing any parameters. Variation comes from the decoding rule and random choices, not from online training during the request.
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Tokenizers use model-specific fragments, so one word can be several tokens and one token can include part of a word plus surrounding formatting.
“Attention weights show exactly why the model answered.”
Attention is one computation within a larger network. Its theoretical behavior depends on assumptions, and an attention visualization is not a complete explanation of the model’s internal decision.
The practical mental model
Think of an LLM as a trained next-token engine embedded in a serving pipeline. The pipeline decides which messages become context, the model scores possible continuations, the decoder chooses one, and the loop repeats until it stops. Tokenization determines the units and the available budget; context determines what evidence and instructions are visible; decoding determines how conservatively the system follows the score distribution; training determines the parameters used in every step. Keeping those layers separate makes it easier to diagnose truncation, surprising formatting, inconsistent answers, and confident mistakes.
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