Artificial intelligence (AI) is the field of building computer systems that can perform tasks that normally require human intelligence. These include understanding language, recognising images, making predictions and deciding what to do next.
An AI engineer builds software products and features on top of AI models, especially large language models, using prompting, retrieval, tool use and evaluation. Companies hire one to turn a promising AI demo into a dependable product.
Explainable AI is the set of methods that make a model's outputs understandable to people. It shows which inputs drove a prediction and how far that prediction can be trusted.
Generative AI refers to models that create new content, such as text, images, code, audio or video, from patterns learned in large amounts of training data.
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.
Machine learning is a branch of AI in which systems learn patterns from data to make predictions or decisions. The behaviour comes from training on examples rather than from rules written by hand.
Multimodal AI refers to models that can take in and reason across more than one kind of data, such as text, images, audio and video, within the same task.
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