Retrieval-Augmented Generation

Expert System

An AI program that emulates the decision-making ability of a human expert by using a knowledge base and inference rules.

Knowledge Graph

A structured representation of real-world entities and the relationships between them, used by AI for reasoning and inference.

Inference Engine

The component of an AI system that applies logical rules to a knowledge base to derive new information or make decisions.

AI Orchestration

The coordination and management of multiple AI models, services, and workflows to achieve complex end-to-end automation.

AI Pipeline

A sequence of data processing and model execution steps that automate the flow from raw data to AI-driven outputs.

AI Model Registry

A centralised repository for storing, versioning, and managing trained AI models across an organisation.

AI Accelerator

Specialised hardware designed to speed up AI computations, including GPUs, TPUs, and custom AI chips.

AI Chip

A semiconductor designed specifically for AI and machine learning computations, optimised for parallel processing and matrix operations.

AI Democratisation

The movement to make AI tools, knowledge, and resources accessible to non-experts and organisations of all sizes.

AI Agent Orchestration

The coordination and management of multiple AI agents working together to accomplish complex tasks, routing subtasks between specialised agents based on capability and context.

Synthetic Data Generation

The creation of artificially produced datasets that mimic the statistical properties of real-world data, used for training AI models while preserving privacy.

AI Memory Systems

Architectures that enable AI agents to store, retrieve, and reason over information from past interactions, providing continuity and personalisation across conversations.

An attention mechanism that selectively computes relationships between a subset of input tokens rather than all pairs, reducing quadratic complexity in transformer models.

The research field focused on ensuring AI systems act in accordance with human values, intentions, and ethical principles.

AI designed to handle specific tasks without possessing self-awareness, consciousness, or true understanding of the task domain.

A theoretical level of AI that surpasses human cognitive abilities across all domains, including creativity and social intelligence.

Empirical relationships describing how AI model performance improves predictably with increases in model size, training data volume, and computational resources.

An inference acceleration technique where a small draft model generates candidate token sequences that are verified in parallel by the larger target model.

An inference strategy that starts with a goal and works backward through rules to determine what facts must be true.

A theoretical form of AI that would have consciousness, self-awareness, and the ability to truly understand rather than simulate understanding.

Text Generation

The process of producing coherent and contextually relevant text using AI language models.