Artificial IntelligenceSoftware systems that perform tasks typically requiring human intelligence, reasoning, perception, language. In practice today, almost always powered by machine learning. Machine LearningA subset of AI where systems learn patterns from data rather than being explicitly programmed. Most modern AI is machine learning. Large Language ModelA machine learning model trained on huge volumes of text to generate and understand language. GPT, Claude, and Gemini are examples. Natural Language ProcessingThe field of AI focused on how computers understand and generate human language. Underpins chatbots, translation, and summarisation. Computer VisionAI systems that interpret images and video, object detection, medical imaging, satellite analysis, and more. Predictive AnalyticsUsing historical data to forecast future outcomes, churn, demand, credit risk, disease outbreak, and beyond. Data PipelineAn automated flow that moves data from source systems into a place where it can be used for analytics or AI. APIApplication Programming Interface. The contract that lets one software system talk to another. AutomationUsing software to perform tasks that previously required human effort, often, but not always, powered by AI. Prompt EngineeringThe craft of writing instructions that get useful, reliable output from a large language model. RAGRetrieval-Augmented Generation. A technique where an AI model retrieves relevant facts from your own data before generating an answer. Fine-TuningAdapting an existing AI model by continuing to train it on your own domain-specific data. HallucinationWhen an AI model generates text that sounds confident but is factually wrong. A core risk to design against. AI GovernanceThe policies, controls, and processes that ensure AI is used responsibly, safely, and in line with regulation. Data SovereigntyThe principle that data is subject to the laws of the country where it is collected or stored, critical for African government workloads. Edge AIRunning AI on-device rather than in the cloud, useful where connectivity is limited or latency is critical. Federated LearningA training approach where models learn from data across many locations without the data ever leaving those locations. Digital TwinA live virtual model of a physical system, used to simulate, monitor, and optimise real-world operations. AI ReadinessThe degree to which an organisation's data, systems, people, and processes are prepared to adopt AI meaningfully. Responsible AIA discipline covering fairness, transparency, safety, privacy, and accountability across the full AI lifecycle. 
