AI is the broad field of building systems that mimic human intelligence. Machine learning (ML) is a subset where models learn from data rather than explicit rules. Deep learning is a subset of ML that uses multi-layer neural networks — it powers most modern language and vision models.
An LLM is a neural network trained on vast amounts of text to predict the next token in a sequence. Through this process it develops internal representations of language, facts, and reasoning patterns. At inference time, it generates text autoregressively — one token at a time — based on the context (prompt) provided.
Supervised learning trains on labelled input-output pairs. Unsupervised learning finds structure in unlabelled data (e.g. clustering). Reinforcement learning trains an agent to maximise reward through trial and error in an environment. Most LLMs are pre-trained with self-supervised learning, then fine-tuned with reinforcement learning from human feedback (RLHF).
An embedding is a dense numeric vector that represents data (text, images, etc.) in a high-dimensional space, where semantically similar items cluster together. They are the foundation of semantic search, retrieval-augmented generation (RAG), and recommendation systems.
The transformer is the neural network architecture underpinning most modern LLMs. Its key innovation is self-attention — each token can attend to every other token in the context window simultaneously, capturing long-range dependencies far more effectively than older RNNs or LSTMs.
