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AI & Cloud Glossary
Understand the term. See it in action. Know what to learn next.
Explore AI, data and cloud concepts through plain-language explanations, practical examples and useful diagrams. Start with a learning path or jump straight to a topic.
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Use AI at work
Understand what AI produces, then learn to guide and check it.
Build an AI application
Move from models and retrieval to tools and agent behaviour.
Connect data and cloud
Follow the foundations behind analytical applications.
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AI foundations
Machine Learning (ML)
Machine learning is a branch of AI in which a model learns patterns from data to perform a task, rather than relying only on explicitly written rules. Its usefulness depends on how well those patterns work on relevant new examples, not just the training data.
Neural Network
A neural network is a machine-learning model made of connected computational units that transform inputs using learned parameters. Layers combine these transformations to represent patterns; training adjusts parameters, while inference uses them to produce an output.
Foundation Model
A foundation model is trained on broad data at scale and can be adapted to a range of downstream tasks. It provides a reusable starting point, not a finished application or a guarantee that it will perform well in a particular organisation.
Generative AI
Generative AI is a category of AI that produces content such as text, images, code or audio from learned patterns and supplied inputs. A generated output is a candidate response, not automatically a retrieved fact, an original invention or a verified result.
Large Language Model (LLM)
A large language model (LLM) is a machine-learning model trained on large amounts of language data to learn patterns that support tasks such as text generation. Many contemporary LLMs generate responses by predicting successive tokens from the input and the text already produced.
Natural Language Processing (NLP)
Natural language processing (NLP) is the field of computing concerned with analysing, interpreting and generating human language. It includes tasks such as classification, extraction, translation and summarisation, using methods ranging from rules to large language models.
Transformer Architecture
A transformer is a neural-network architecture that uses attention mechanisms to combine information across positions in a sequence. It combines attention with other components, including feed-forward transformations and position information; attention alone is not the whole model.
Multimodal AI
Multimodal AI works with more than one type of information, such as text and images or audio and video. A multimodal application can relate a question to visual or other non-text input; its supported input and output types depend on the model and system design.
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Building AI applications
Prompt Engineering
Prompt engineering is the practice of designing and testing the instructions, context and examples supplied to an AI model to achieve a defined task. It involves evaluating outputs and refining the input, rather than relying on a particular phrase to guarantee a good response.
Embeddings
Embeddings are numerical representations of data, usually expressed as vectors. An embedding model learns representations that place related items near one another according to a chosen similarity measure. Applications use them for tasks such as semantic search, recommendations and grouping similar content.
Vector Database
A vector database stores and searches vector representations such as embeddings. It helps applications find similar items and manage their associated records. Vector search can be provided by a specialised database or added to an existing database through an extension or built-in capability.
Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation (RAG) is a method that supplies retrieved information to a generative model when it answers a question. It connects generation to external sources, such as product documentation or company policies, without requiring the model to be retrained for each source update.
Fine-Tuning
Fine-tuning is additional training that adapts a pretrained model using a selected dataset. It changes some or all trainable parameters to improve behaviour for a particular task or domain. For language models, examples may teach a consistent response format, classification style or task-specific behaviour.
AI Agent
An AI agent is a system that selects actions towards a goal, observes what happens and uses that feedback to decide what to do next. In an LLM-based agent, a language model helps choose actions while application code manages tools, permissions and execution.
Agentic AI
Agentic AI describes an approach in which AI systems choose and carry out steps towards a goal, adjusting their actions from feedback. The term describes how a system behaves; an AI agent is a component or system that exhibits that behaviour.
Tool Calling
Tool calling is a mechanism through which a model requests a named operation with structured arguments, such as an order lookup. A tool runtime executes the operation and returns its result; generating a tool request does not itself execute the action.
Model Context Protocol (MCP)
Model Context Protocol (MCP) is an open protocol for connecting AI applications to external capabilities and context. An MCP host manages clients that connect to servers exposing supported tools, resources or prompts.
LangChain
LangChain is an open-source framework for building applications and agents around language models. It provides model and tool integrations and an agent harness; it is not itself a language model or a source of business data.
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Quality and responsible use
AI Hallucination
An AI hallucination is generated content that is false or unsupported while being presented as an answer. In a document-based task, this can include details not supported by the supplied source, even when those details sound plausible. Fluent wording and confident tone do not establish accuracy.
AI Bias
AI bias is a systematic tendency in an AI system’s data, design or use that can produce skewed results or unfair effects. Evaluating bias requires attention to the affected people and context, not just a single overall accuracy score.
Explainable AI (XAI)
Explainable AI refers to methods that help people understand a model’s behaviour or a particular output. An explanation should be appropriate to its audience and faithful to what it describes; a plausible story alone is not evidence of how a model reached its result.
Responsible AI
Responsible AI is the practice of considering the effects of AI on people throughout design, deployment and use, and acting to reduce foreseeable harms. It connects goals such as reliability, fairness, privacy and accountability to concrete decisions and evidence.
AI Governance
AI governance is the set of responsibilities, policies and decision processes through which an organisation oversees AI across its lifecycle. It establishes who can approve a use, what evidence is required and who responds when the system causes problems.
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Data and analytics
Data Pipeline
A data pipeline is a sequence of processes that moves data from sources to destinations, often validating or transforming it along the way. A reliable pipeline manages failures, duplicates and freshness as well as transporting records.
Data Warehouse
A data warehouse is a system organised for analytical queries and reporting across integrated data, often including history from multiple sources. Its usefulness depends on consistent definitions and modelling, not merely collecting everything in one database.
Business Intelligence (BI)
Business intelligence (BI) is the practice of organising, analysing and presenting business data to support decisions. It combines agreed measures, data models and reporting; an attractive dashboard alone does not establish that its numbers are meaningful.
MLOps
MLOps is the practice of developing, releasing and operating machine-learning systems with repeatable processes and monitoring. It connects code, data, model versions and evaluation so that a deployed prediction service can be maintained and investigated.
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Cloud and delivery
Cloud Computing
Cloud computing provides on-demand network access to shared, configurable computing resources that can be provisioned and released with limited manual intervention. It changes how resources are obtained and operated; it does not remove the need to manage applications, access, data and costs.
Infrastructure as a Service (IaaS)
Infrastructure as a Service (IaaS) is a cloud service model that supplies computing, storage and networking capacity while the customer manages the guest operating system and applications. The exact responsibility boundary depends on the provider and service.
Serverless Computing
Serverless computing is an execution and service model in which a provider manages server provisioning and much of the underlying capacity operation. Developers configure application behaviour and service limits rather than routinely administering individual servers.
Cloud Native
Cloud native is an approach to designing and operating workloads for repeatable change, resilience and observability in dynamic computing environments. It is a set of architectural and operational practices, not simply the fact that an application is hosted in a cloud.
Docker
Docker is a set of tools for building, distributing and running containerised applications. An image packages application files and dependencies; a container is a running instance using that image with runtime configuration and isolation.
Kubernetes (K8s)
Kubernetes is an open-source platform for orchestrating containerised workloads. Its control loops work to bring observed cluster state towards declared desired state, such as maintaining a specified set of application replicas.
DevOps
DevOps is an approach that connects software development and operations through shared responsibility, automation and feedback. Its purpose is to improve how changes are delivered and operated, rather than simply increase the number of releases.
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