Learning path: Language models

Your path

1. Neural networksDirect prerequisite

   An artificial neural network is a mathematical function built from many simple units arranged in layers: each unit multiplies its inputs by weights, adds a bias and passes the total through an activation function. Stacking layers lets the network represent patterns — such as XOR — that no single linear unit can. The weights are not written by hand but learned from data, usually by back-propagation and gradient descent; the networks are inspired by the brain but are not realistic models of it.

   Take the Neural networks quiz

2. Training dataDirect prerequisite

   Training data is the set of examples a machine-learning model learns from, and it shapes everything the model can and cannot do. Data is usually split into training, validation and test sets so that the model is judged on examples it never learned from. Gaps, mislabelled examples and skewed samples in the data become gaps, errors and biases in the model, which is why datasets need documenting and checking like any other scientific instrument.

   Take the Training data quiz

3. Language modelsYour goal

   A language model is a system that assigns probabilities to sequences of words — in practice, it repeatedly predicts a probability for each possible next token (a word or piece of a word) given the text so far. Large language models are neural networks with billions of parameters trained on vast amounts of text, and they can carry out many tasks described in plain language. Because they generate statistically likely text rather than looking facts up, they can state false things confidently ('hallucination'), and they reproduce biases in their training data.

   Take the Language models quiz

Where to go next

Concepts that build directly on Language models:

• AI agents: An AI agent is a system that perceives its environment, decides what to do and takes actions towards a goal, repeating that loop rather than giving a single answer. Today's best-known agents wrap a language model in a loop with memory, planning and tools — search, calculators, code, a computer's mouse and keyboard — so it can carry out multi-step tasks. Agents have improved quickly on benchmarks (as of the AI Index 2026) but still fail often, and acting in the world raises new risks such as compounding errors and prompt injection, so their autonomy needs limits and human oversight.