AI & Cloud Glossary
Plain-English definitions of the AI, generative AI, machine learning, and cloud computing terms your team needs to understand. Updated for 2026.
34 terms · 6 categories · Built for L&D managers, business leaders, and corporate training teams
Generative AI
Large Language Model (LLM)
AI systems trained on massive text datasets that can generate, summarise, translate, and reason about language.
Read definition →Prompt Engineering
The skill of designing inputs (prompts) that produce accurate, useful, and consistent outputs from AI systems.
Read definition →Retrieval-Augmented Generation (RAG)
Technique that grounds LLM responses in verified documents, reducing hallucination in enterprise AI applications.
Read definition →Generative AI
AI models that create new content — text, images, code, audio — rather than only classifying or predicting.
Read definition →AI Hallucination
When an AI model produces confident but factually incorrect or fabricated output.
Read definition →Fine-Tuning
Further training a pre-trained model on a smaller, domain-specific dataset to specialise its behaviour.
Read definition →Transformer Architecture
The neural network design powering every major LLM — GPT-4, Claude, Gemini — using self-attention to understand language.
Read definition →Multimodal AI
AI systems that process and generate text, images, audio and video together in a single model.
Read definition →Foundation Model
Large AI models trained on broad data at scale that can be adapted to many tasks without retraining from scratch.
Read definition →Agentic AI
Agentic AI
AI systems that reason, plan and take multi-step autonomous actions to complete a goal — beyond single Q&A.
Read definition →AI Agent
An autonomous AI system that perceives its environment, makes decisions, and takes actions to achieve a goal.
Read definition →LangChain
The most widely used open-source Python framework for building production LLM applications, RAG pipelines and agents.
Read definition →Tool Calling (Function Calling)
Capability that allows LLMs to invoke external APIs and functions — enabling AI to take real-world actions.
Read definition →Model Context Protocol (MCP)
Anthropic's open standard for connecting AI models to tools and data sources — the USB-C of AI integrations.
Read definition →Vector Database
Specialised database that stores embeddings to enable semantic search — a core component of RAG and AI agents.
Read definition →Embeddings
Numerical representations of text or data that capture semantic meaning — used in search, RAG and recommendations.
Read definition →Machine Learning
Machine Learning (ML)
Systems that learn patterns from data to make predictions or decisions without being explicitly programmed.
Read definition →MLOps
Practices and tools for deploying, monitoring, and maintaining machine learning models in production reliably.
Read definition →Natural Language Processing (NLP)
AI techniques that allow computers to understand, interpret, and generate human language.
Read definition →Neural Network
Computational systems loosely modelled on the human brain, used to recognise patterns in data.
Read definition →Cloud & DevOps
Cloud Computing
Delivery of computing resources — servers, storage, databases, AI services — over the internet on pay-as-you-go basis.
Read definition →Infrastructure as a Service (IaaS)
Cloud service model providing virtualised computing infrastructure over the internet.
Read definition →Serverless Computing
Cloud execution model where the provider manages server infrastructure, billing only for actual execution time.
Read definition →Kubernetes (K8s)
Open-source system for automating deployment, scaling, and management of containerised applications.
Read definition →DevOps
Practices combining software development and IT operations for faster, more reliable software delivery.
Read definition →Docker
Platform that packages applications and dependencies into portable containers that run identically anywhere.
Read definition →Cloud Native
Approach to building applications designed to exploit cloud capabilities — containers, microservices, auto-scaling.
Read definition →Data & Analytics
Business Intelligence (BI)
Technologies and practices for turning raw business data into actionable insights via dashboards and reports.
Read definition →Data Warehouse
Centralised repository of historical business data from multiple sources, optimised for analytical queries.
Read definition →Data Pipeline
Automated workflow that moves and transforms data from source systems to analytical destinations.
Read definition →AI Ethics
AI Governance
Frameworks, policies, and processes for ensuring AI systems are safe, ethical, compliant, and accountable.
Read definition →Responsible AI
Principles and practices for building AI that is fair, transparent, safe and aligned with human values.
Read definition →AI Bias
Systematic unfairness in AI outputs towards certain groups — causes, detection methods, and mitigation.
Read definition →Explainable AI (XAI)
Methods for making AI model decisions understandable to humans — SHAP, LIME, counterfactual explanations.
Read definition →Want your team to go beyond definitions?
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