Large Language Models
How LLMs are built, trained and used in production.
AI Hallucination
A hallucination is an LLM output that is fluent and plausible but factually incorrect or unsupported by any reliable source.
AI Tokens
A token is the unit of text an LLM processes — typically 3-4 characters or roughly three-quarters of an English word. Prompts, replies and pricing are all measured in tokens.
Context Window
The context window is the token budget an LLM can read and write within a single request, covering the prompt, retrieved data, previous turns and the response itself.
Fine-tuning
Fine-tuning is additional training applied to a pretrained model on curated examples so the model specialises in a task, tone, format or domain.
Foundation Model
A foundation model is a large-scale, general-purpose model pretrained on broad data and designed to be adapted to many downstream tasks via prompting, fine-tuning or tool use.
LLM
An LLM is a neural network trained on very large text datasets to predict the next token in a sequence, which enables general-purpose language understanding and generation.
Reinforcement Learning
Reinforcement learning is a machine learning paradigm in which an agent learns to make decisions by receiving reward signals for its actions in an environment.
Transformer Model
A transformer is a neural network architecture built around self-attention, which enables efficient modelling of long-range dependencies in sequences.