A transformer is a neural network architecture that uses attention to weigh the relationships between all parts of an input. It is the basis of most modern language models.
Computer vision is the field of AI that lets software interpret images and video. Typical tasks are classifying objects, detecting defects, reading text and tracking movement.
A context window is the maximum amount of text, counted in tokens, that a language model can take into account in one request. It covers the instructions, the conversation, any retrieved documents and the reply.
Deep learning is a type of machine learning that uses neural networks with many layers to learn complex patterns. It excels with large amounts of unstructured data such as images, audio and text.
A large language model (LLM) is a neural network trained on very large amounts of text to predict and generate language. It powers chat assistants, summarisers, coding tools and AI agents.
In AI, tokenization is splitting text into small units called tokens, such as words or word pieces, which a language model reads and generates. Token counts set how much text fits in a model's context window and usually drive the cost of each API call.
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