Glossary

AI Hallucination refers to instances where an AI model generates information that sounds plausible but is factually incorrect or entirely fabricated. It's a known limitation of current language models and highlights the need for human oversight.

AI Search uses artificial intelligence to understand the intent behind a user’s query and deliver relevant, contextual results. Unlike traditional keyword-based search, it uses natural language processing and machine learning to interpret meaning, rank content, and generate direct answers.

AGI is a theoretical form of AI that possesses the ability to understand, learn, and apply knowledge across a wide range of tasks, similar to human intelligence. Unlike today’s narrow AI systems, AGI would be capable of flexible, self-directed reasoning.

A context window defines how much input text an AI model can consider at one time when generating a response. If a prompt exceeds the window, older parts may be ignored, which can affect relevance and coherence.

Deep Learning is a type of machine learning that uses neural networks with many layers to model complex patterns in large datasets. It’s especially effective in image recognition, natural language generation, and speech processing.

Deep Research involves the thorough exploration and analysis of a topic using diverse and credible sources, often to generate high-quality or authoritative content. In the context of AI, it may refer to either human-led research or AI-assisted investigation supported by verified data.

Inference is the stage where an AI model, after training, applies what it has learned to generate responses or predictions. For users, this is the real-time output they see after submitting a query or prompt.

A Large Language Model is a type of AI trained on massive amounts of text data to understand and generate human-like language. These models, such as GPT-4, can write articles, answer questions, summarise content, and more by predicting the next word based on context.

Machine Learning is a branch of artificial intelligence where systems learn patterns from data to make predictions or decisions without being explicitly programmed. It powers many AI tools, including those that generate text, analyse images, or recommend content.

Multimodal AI can interpret and generate content across multiple types of input and output, such as text, images, and audio. This makes it more versatile and useful in complex real-world applications like video captioning or voice-driven design.

Natural Language Processing is the field of AI focused on enabling machines to understand, interpret, and generate human language. It forms the backbone of tools like chatbots, translators, and voice assistants.

Prompt Engineering is the practice of designing effective inputs (prompts) to guide AI models in producing accurate, relevant, or creative outputs. It plays a critical role in getting the most out of generative AI tools like ChatGPT.

SEO is the process of optimising online content so it ranks higher in search engine results, helping attract more organic traffic. It involves techniques like keyword use, backlinking, and content structuring.

In the context of AI language models, a token is a piece of text, often a word or part of a word, that the model uses to process and generate language. The number of tokens affects how much context the AI can understand at once.