Short answer: an LLM turns your sentence into token IDs, maps those IDs to vectors, adds information about their order, and passes the result through many Transformer layers. Self-attention mixes relevant context at each position; feed-forward layers refine it. An output layer scores every possible next token, a decoding rule selects one, and the model repeats this loop until it stops.
1. Your sentence becomes tokens
Tokenization is not the same as splitting on spaces
Consider “The cat sat on the mat.” The model’s tokenizer converts the text into a sequence of integer token IDs. The exact sequence depends on the model and its vocabulary: a word may be one token, several subword tokens, or a token that includes a leading space. The period can be its own token or part of a neighboring token.
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Common tokenizer families include byte-pair encoding (BPE), Unigram and WordPiece. Subword tokenization keeps the vocabulary manageable while allowing an unfamiliar or rare word to be assembled from pieces the model already knows. This is also why token counts differ between languages, spellings, code and ordinary prose.
IDs are labels, not meanings
A token ID is only an index in a vocabulary. At this stage, the number assigned to cat has no useful geometric meaning. Meaningful structure appears when the ID is looked up in the model’s learned embedding table.
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2. IDs become vectors with position
Embeddings provide a working representation
The embedding table stores a high-dimensional vector for each token. During training, these vectors are adjusted so that patterns useful for predicting text can be represented. A token’s representation can therefore participate in relationships involving syntax, spelling, topic and usage, rather than being treated as an isolated dictionary entry.
Position tells the model what came first
Self-attention alone does not inherently know whether cat sat appeared before or after sat cat. Positional information is combined with the token representations so the network can distinguish order, distance and repeated occurrences. The precise positional method varies by architecture.
3. Transformer layers mix the context
A complete Transformer stacks multiple self-attention layers, as Google’s documentation explains. The original 2017 Transformer paper described the design as a network “based solely on attention mechanisms,” dispensing with recurrence and convolutions.
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At each position, the layer computes how strongly that position should use information from other positions that are visible to it. For the token representing sat, useful context might include cat as the likely subject and The as its determiner. For the final position, earlier words can help predict what grammatical or semantic continuation is plausible.
A useful theoretical description from Li and coauthors (AISTATS 2024) separates attention into hard retrieval of high-priority context tokens and soft composition of information from those tokens. Real implementations perform continuous numerical operations, not a literal database lookup.
Feed-forward layers refine each position
After attention mixes information across positions, a feed-forward network transforms the representation at each position independently. Repeated attention and feed-forward stages let the model build progressively richer features: local word relationships in early processing and longer-range syntactic, semantic or task-related patterns in later processing. Residual connections and normalization help these stacked transformations remain trainable; exact details differ among model families.
4. The output head predicts one next token
Logits become probabilities
For next-token generation, the model uses the representation at the current end of the sequence to produce one score, called a logit, for every token in its vocabulary. A probability transformation converts those scores into a distribution. High probability means “more compatible with the learned parameters and this context,” not “verified fact.”
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- Greedy decoding: choose the highest-probability token.
- Temperature: reshape the distribution; lower values make choices more concentrated, while higher values make less-likely choices more available.
- Top-p sampling: sample from the smallest group of tokens whose cumulative probability reaches a chosen threshold.
- Stopping rules: stop at an end-of-sequence token, a configured length limit, or another application-defined condition.
The decoding policy is separate from the neural network’s learned parameters. Two runs with the same prompt can therefore differ when sampling is enabled.
5. The sentence-to-answer loop
Inference is autoregressive: each selected token becomes part of the next input. For a prompt ending with “The cat sat on the mat”, the process is:
- Tokenize the complete prompt into the model’s token IDs.
- Look up embeddings and add positional information.
- Pass the sequence through the stacked Transformer blocks.
- Compute logits and probabilities for the next token.
- Select one token with the configured decoding policy.
- Append that token to the context and run the model again.
- Continue until a stopping condition is reached.
A long answer is therefore generated one token at a time, even though each pass uses the growing context. Systems commonly cache intermediate attention values so earlier work does not have to be recomputed in full at every step; the exact caching implementation is model- and serving-system dependent.
6. Training teaches the prediction skill
Pretraining updates the parameters
During pretraining, text examples are converted into token sequences. The model is asked to predict a masked token or the next token, depending on the objective. Its prediction is compared with the target using a loss function, and gradient-based optimization adjusts the parameters to reduce that loss over many examples.
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Repeated prediction forces the network to capture regularities in spelling, grammar, facts represented in the data and relationships between words. Microsoft Learn describes this as training a neural network on massive text collections to predict the next token; Google’s example similarly hides tokens and trains the model to recover them.
Inference does not keep learning from each prompt
At inference time, the parameters are held fixed. Your prompt changes the temporary context and the resulting probabilities, but it does not by itself update the model’s weights. A separate training or fine-tuning process is required to change those parameters.
7. What “large” means
“Large” refers to a combination of parameter count, training data and computation, not a single universal cutoff.
| Scale dimension | What it represents | Why it matters |
|---|---|---|
| Parameters | Learned numerical weights in embeddings, attention, feed-forward layers and other components | More capacity can represent more patterns, while increasing memory and serving cost |
| Training data | Token sequences used to estimate useful parameters | Broader and cleaner data can expose more linguistic and factual patterns |
| Compute | Hardware time and arithmetic used during optimization and serving | Determines how much data and how many parameter updates can be processed |
OpenAI reported in 2020 that cross-entropy loss followed power-law trends with model size, dataset size and compute across more than seven orders of magnitude. That finding describes predictive loss and compute-efficient training; it does not guarantee factuality, reliability or human-like reasoning.
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8. Different LLM designs answer different jobs
“LLM” covers several Transformer arrangements. Compare them by tokenization, context handling, objective and decoding rather than by parameter count alone.
| Architecture | Typical objective | Strength | Generation behavior |
|---|---|---|---|
| Decoder-only | Causal next-token prediction | Continuation, dialogue and code generation | Produces output autoregressively, token by token |
| Encoder-only | Masked-token or task-specific representation learning | Classification, retrieval and extracting meaning from complete text | Usually scores or represents text rather than freely generating a continuation |
| Encoder-decoder | Transforming an input sequence into an output sequence | Translation, summarization and other input-to-output tasks | The decoder generates while attending to encoded source information |
Models also differ in vocabulary design, context-window capacity, how attention is computed, parameter efficiency, latency and memory requirements. A larger model is not automatically the best choice for every workload.
9. What the model is—and is not—doing
It generates from learned statistical structure
The model’s fluent response comes from interactions among learned representations and the current context. It is not necessarily retrieving a complete answer from a stored document. If an application adds a search or retrieval system, that external component can supply documents; the base language model still converts the resulting context into token probabilities.
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Fluency is not a truth guarantee
The training objective rewards plausible continuation, not independent verification. A model can produce a confident-sounding error, especially when the prompt is ambiguous, the needed information is absent or the data contained contradictions. Check consequential claims against authoritative sources and state the relevant date or jurisdiction.
Context limits change what can be used
Only the text available within the model’s context limit can directly influence a generation pass. Long prompts may be truncated, summarized or handled with a system-specific strategy. The limit and handling policy vary by model and product.
10. A compact mental model
- Tokenizer: text to model-specific IDs.
- Embedding and position: IDs plus order to vectors.
- Transformer blocks: attention mixes context; feed-forward layers transform it.
- Output head: final representation to logits and probabilities.
- Decoder: probabilities to one selected token.
- Loop: append the token and repeat until stopping.
This chain explains both the strength and the limits of an LLM: it can model extraordinarily rich patterns in sequence data, yet every answer remains a sequence of probability-guided predictions conditioned on the context it received.
Further reading
For implementation-level detail on tokenization, embeddings, positional information, attention, logits, sampling and autoregressive generation, Manning lists How Large Language Models Work by Edward Raff, Drew Farris and Stella Biderman (June 2025, ISBN 9781633437081, 200 pages).
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