How Large Language Models work: an overview
Large Language Models (LLMs) are neural networks trained to predict the next token in a sequence. By doing this at scale over massive datasets, they learn patterns of language and world knowledge that enable useful behaviors like answering questions, writing code, and summarizing text. They operate by converting text into tokens, mapping tokens to vectors, mixing information with self‑attention inside stacked Transformer blocks, and projecting back to token probabilities for generation.

Architecture at a glance

  • Tokenizer turns text into discrete token IDs (including special tokens like BOS/EOS).
  • Embedding layer maps each token ID to a dense vector; positional signals encode order.
  • Transformer stack repeats: LayerNorm → Multi‑Head Self‑Attention → Residual, then LayerNorm → MLP → Residual.
  • LM head projects the final vectors to vocabulary logits; softmax gives probabilities for the next token.

Training in practice

  • Objective: next‑token prediction; loss is typically cross‑entropy over the vocab.
  • Optimization: backprop with optimizers like AdamW; batches and many epochs over diverse data.
  • Scale: more parameters and data generally improve capability, within compute constraints.
  • Adaptation: fine‑tuning or instructions/RLHF can steer behavior; evaluation uses held‑out sets.

Why LLMs are useful

  • General‑purpose text interface: chat, Q&A, code generation, summarization, translation.
  • In‑context learning: models can follow patterns from examples directly in the prompt.
  • Composability: chain model calls with tools, retrieval, or frameworks to solve complex tasks.

Limits and trade‑offs

  • Hallucinations: models can produce fluent but incorrect content; verification is key.
  • Bias and safety: outputs reflect training data; alignment and guardrails are important.
  • Latency/cost: inference scales with sequence length and parameter count.

Guided animation: the LLM pipeline

Use the controls to step through tokenization, embeddings, self‑attention, Transformer blocks, training, and inference.

Tokenization
Text is split into tokens—the basic units the model understands. Many real systems use subword pieces (e.g., “believ” + “able”) and special tokens for control.
Speed1.00×

Input text

The quick brown fox jumps over the lazy dog .

We visualize word‑like tokens; real tokenizers produce compact subword IDs for efficient coverage.

Tokens

The
quick
brown
fox
jumps
over
the
lazy
dog
.
Token IDs are just numbers; modeling uses IDs to index embeddings.

Deeper dive: what’s happening

  • Split the input into pieces (tokens). For example, “unbelievable” might become “un”, “believ”, “able”.
  • Attach special tokens like BOS (begin‑of‑sequence) or EOS (end‑of‑sequence) in real pipelines.
  • Produce token IDs (integers) for the next layer.

Why it matters

  • Handles rare or unknown words through subword pieces, reducing out‑of‑vocabulary issues.
  • Defines the maximum sequence length and shapes compute/memory costs.
  • Creates a stable, discrete interface between text and neural layers.

Note: We illustrate simplified shapes; real models use subword tokenization and high‑dimensional vectors repeated over many layers.