# AI, Data and Engineering Glossary Plain-English definitions for artificial intelligence, data and engineering terms used in modern organisations. Rodan · Field Intelligence # Glossary Plain-English definitions for the AI, data and engineering terms that appear in strategy conversations, product roadmaps and production systems. [A](#letter-a) [B](#letter-b) [C](#letter-c) [D](#letter-d) [E](#letter-e) [F](#letter-f) [G](#letter-g) [H](#letter-h) [I](#letter-i) [J](#letter-j) [K](#letter-k) [L](#letter-l) [M](#letter-m) [N](#letter-n) [O](#letter-o) [P](#letter-p) [Q](#letter-q) [R](#letter-r) [S](#letter-s) [T](#letter-t) [U](#letter-u) [V](#letter-v) [W](#letter-w) [X](#letter-x) [Y](#letter-y) [Z](#letter-z) ## A ### Agentic AI AI designed to pursue a goal through a sequence of actions rather than produce a single response. Agentic systems can plan, use tools and adapt to results, which makes permissions, monitoring and clear stopping conditions essential. ### AI agent A software component that uses an AI model to decide what action to take within a defined environment. A useful agent needs bounded objectives, approved tools, access controls and a reliable way to recover when an action fails. ### AI evaluation (evals) A structured method for measuring whether an AI system performs acceptably on representative tasks. Evals should test accuracy, safety, consistency, cost and operational outcomes before deployment and as the system changes. ### AI governance The roles, policies and controls used to decide how AI may be selected, built and operated. Effective governance assigns accountability, classifies risk and requires evidence proportionate to the consequences of each use case. ### AI guardrails Technical and procedural controls that constrain an AI system’s inputs, outputs or actions. Guardrails can reduce known risks, but they do not replace evaluation, secure system design or accountable human oversight. ### AI hallucination An output that appears plausible but is unsupported, incorrect or invented. Hallucination risk is managed through grounding, constrained tasks, source evidence, evaluation and review rather than prompting alone. ### AI observability The ability to inspect how an AI system behaves in production using traces, quality measures, costs and user feedback. It helps teams find failures, investigate unexpected outputs and understand whether performance is changing. ### Algorithm A defined set of instructions that turns an input into an output. Algorithms can be simple business rules or complex statistical processes; what matters operationally is whether their purpose, inputs and limitations are understood. ### Algorithmic bias A systematic tendency for an automated system to produce unfair or distorted outcomes. Bias can enter through historical data, labels, design choices or how a model is deployed, so testing must cover affected groups and real operating conditions. ### Artificial intelligence (AI) A broad term for computer systems that perform tasks associated with human intelligence, such as interpreting language, recognising patterns or supporting decisions. In production, AI is one component of a wider system that also needs reliable data, controls, interfaces and accountable people. ## B ### Big data Data whose volume, speed or variety exceeds what conventional tools can handle efficiently. The phrase does not imply value: large datasets only become useful when they are trustworthy, accessible and connected to a clear decision or workflow. ## C ### Chatbot A software interface that lets people interact with a service through written or spoken conversation. Modern chatbots often use language models, but useful deployment still requires bounded knowledge, clear escalation routes and access controls. ### Commercial insights Evidence that changes a commercial decision, rather than reporting that only describes what happened. Good insight connects trustworthy data to a specific action involving customers, pricing, operations, investment or growth. ### Computer vision Methods that enable computers to interpret images and video. Typical applications include inspection, classification and detection, but performance depends heavily on representative image data and how uncertain cases are reviewed. ### Context window The amount of information a language model can consider during one interaction. A larger context window can accommodate more material, but it does not guarantee that every detail will be recalled or used correctly. ## D ### Data Recorded facts, measurements, observations or representations that can be processed and analysed. Data becomes operationally valuable when its meaning, ownership, quality and permitted uses are clear. ### Data-centric engineering An engineering approach that improves system performance by treating data quality, structure and feedback as first-class design concerns. It shifts attention from repeatedly changing models to building dependable data pipelines, labels, evaluation sets and monitoring. ### Data governance The decisions, roles and controls that determine who can use which data and for what purpose. Effective governance makes ownership, quality, access, retention and accountability part of everyday operations rather than a one-off compliance exercise. ### Data lineage A record of where data originated, how it changed and which systems or reports use it. Lineage helps teams assess trust, investigate errors and understand the impact of changing a source or transformation. ### Data maturity An organisation’s ability to collect, govern, connect and use data reliably. Maturity is demonstrated by repeatable decisions and operating practices, not by the number of tools purchased or dashboards produced. ### Data product A reusable, managed data asset designed to meet a defined user need. Like a software product, it should have an owner, documented meaning, quality expectations and a process for change. ### Data science The practice of using statistics, computation and domain knowledge to learn from data. Its outputs may include analysis, experiments or predictive models, but they create value only when connected to a real decision and measured in use. ### Dataset A structured collection of related data used for analysis, reporting or model development. A dataset should have a defined source, scope, owner and quality standard so users know what conclusions it can support. ### Deepfake Synthetic or manipulated media designed to make a person appear to say or do something they did not. Deepfakes create fraud, misinformation and identity risks that require verification processes as well as detection technology. ### Deep learning A branch of machine learning based on neural networks with many processing layers. It powers advances in language, vision and speech, but often requires substantial data and computing resources and can be difficult to interpret. ### Digital twin A digital representation of a physical asset, process or environment that is updated using real-world data. Organisations use digital twins to monitor conditions, test scenarios and improve operations without disrupting the real system. ## E ### Embedding A numerical representation that captures selected meaning or similarity in data such as text, images or products. Embeddings support semantic search, recommendations and retrieval by placing related items near one another mathematically. ### Encryption The transformation of readable information into a protected form that can only be accessed with the correct key. Encryption protects data in transit and at rest, but it must be paired with sound identity, access and key-management practices. ### Explainable AI Methods that help people understand why an AI system produced a particular result. The required explanation depends on the audience and consequence, ranging from source evidence to model-level analysis and decision records. ## F ### Fine-tuning Further training a pre-existing model on selected examples to change its behaviour for a domain or task. Fine-tuning can improve consistency, but it requires suitable data, evaluation and ongoing maintenance. ### Foundation model A large model trained on broad data that can be adapted to many downstream tasks. Language and vision models are common examples; organisations typically configure, ground or fine-tune them rather than train one from scratch. ### Forward-deployed engineering A delivery model in which engineers work closely with domain teams inside the operating problem. The aim is to shorten the distance between requirements, technical decisions, user feedback and a production system the organisation can own. ## G ### Generative adversarial network (GAN) A machine-learning architecture in which one network generates examples and another evaluates whether they resemble real data. GANs can create realistic images and synthetic data, although newer generative methods are now used for many of the same tasks. ### Generative AI AI systems that create new text, images, audio, video or code from learned patterns. Business use requires more than content generation: outputs need grounding, evaluation, permissions and human review appropriate to their consequences. ## H ### Human-in-the-loop (HITL) A system design in which people review, correct or approve parts of an automated process. Human involvement is most useful when responsibilities, escalation thresholds and the evidence available to reviewers are explicitly designed. ## I ### Inference The process of using a trained model to produce a prediction or generated output from new input. In production, inference design affects response time, cost, privacy, reliability and where a model can run. ## J ### JSON A lightweight text format for representing and exchanging structured data. JSON is widely used by APIs, applications and AI tools because both people and software can read it consistently. ## K ### Knowledge graph A structured representation of entities and the relationships between them. Knowledge graphs help organisations connect fragmented information, preserve context and support search, analysis and AI systems with explicit relationships. ## L ### Large language model A model trained on large collections of text to predict and generate language. LLMs can support search, drafting, extraction and workflow automation, but they can also produce plausible errors and must not be treated as an authoritative database. ### LLMOps The practices used to deploy, evaluate, monitor and maintain applications built around language models. It extends conventional software operations to cover prompts, model versions, retrieval, quality, safety and usage cost. ## M ### Machine learning (ML) A branch of AI in which systems learn patterns from examples rather than relying only on hand-written rules. A production ML system includes the data, model, software, monitoring and operating process needed to keep those patterns useful over time. ### MLOps The engineering practices used to deploy, monitor and update machine-learning systems reliably. MLOps connects data, model development and software operations so changes can be tested, traced and reversed. ### Model Context Protocol (MCP) An open protocol for connecting AI applications to tools and external sources of context through a standard interface. MCP can reduce bespoke integration work, while the application remains responsible for authentication, permissions and safe tool use. ### Model drift A decline or change in model behaviour as real-world data and conditions move away from those used during development. Monitoring should detect drift early enough for teams to investigate, retrain, adjust or withdraw the model. ### Multi-agent system (MAS) A system in which multiple autonomous software agents coordinate or compete to complete tasks. Multi-agent designs can separate specialist responsibilities, but they also increase complexity around state, permissions, failure handling and evaluation. ### Multimodal AI AI that can work across more than one type of information, such as text, images, audio or video. Multimodal systems can support richer workflows but require evaluation across each input type and their combinations. ## N ### Natural language processing (NLP) Methods that enable computers to analyse, interpret and generate human language. NLP is used for tasks such as classification, extraction, search, summarisation and conversational interfaces. ### Neural network A machine-learning model made of connected processing units that learn relationships from data. Neural networks are effective for complex patterns but usually need careful evaluation because their internal reasoning is not straightforward to inspect. ## O ### Open source Software whose source code is available under a licence that permits specified forms of use, inspection and modification. Open source can improve flexibility and transparency, but teams still need to assess licence obligations, maintenance, security and operational ownership. ## P ### Production AI system A complete operational system in which AI performs a defined role alongside data pipelines, software, controls, monitoring and people. Production readiness means it can deliver dependable value under real conditions, not merely demonstrate technical potential. ### Prompt engineering The design and testing of instructions and context supplied to a generative model. Good prompts improve behaviour, but dependable applications also need evaluation, structured inputs, source grounding and software controls. ## Q ### Quantisation A technique that reduces the numerical precision of a model so it uses less memory and computing power. Quantisation can make models cheaper and faster to run, but the compressed version still needs evaluation against the original quality standard. ## R ### Reinforcement learning A machine-learning approach in which an agent learns actions through rewards and penalties received from an environment. It is useful for sequential decisions, but the reward design must reflect the real objective or the system may optimise the wrong behaviour. ### Responsible AI The practice of designing and operating AI in ways that are lawful, fair, secure and accountable. It turns broad principles into decisions about data, testing, transparency, oversight and what the system must not do. ### Retrieval-augmented generation (RAG) A technique that retrieves relevant information from an approved source before asking a generative model to respond. RAG can improve relevance and provide source evidence, but retrieval quality and access permissions must also be evaluated. ## S ### Semantic layer A shared business representation that gives data consistent definitions, relationships and measures across tools. It helps prevent teams from calculating the same metric differently in dashboards, analysis and AI applications. ### Small language model (SLM) A language model designed with fewer parameters and lower computing requirements than the largest general-purpose models. SLMs can offer lower cost, faster responses and greater deployment control for focused tasks. ### Synthetic data Artificially generated data designed to reproduce selected properties of real data. It can support testing, simulation and model development where real examples are scarce or sensitive, but it must be checked for realism, bias and privacy leakage. ## T ### Tool calling The ability for an AI model to request that an application execute an approved function, such as searching a system or creating a record. The surrounding software must validate arguments, enforce permissions and decide whether consequential actions require confirmation. ## U ### Unstructured data Information that does not sit neatly in rows and columns, such as documents, emails, images, audio and chat logs. Most organisational knowledge is unstructured, which is why extraction, classification and retrieval are often the first production AI problems. ### Unsupervised learning A machine-learning approach that finds structure in data without labelled answers. It is used for clustering, anomaly detection and exploration, but the resulting groups still need a human interpretation of what they mean operationally. ## V ### Vector database A database designed to store embeddings and find items with similar numerical representations. It is commonly used for semantic search and RAG, where metadata filtering and source permissions remain as important as similarity. ## W ### Workflow orchestration The coordination of tasks, systems and decision points within a repeatable process. In AI-enabled workflows, orchestration controls sequencing, state, retries, approvals and what happens when a model or external service fails. ## X ### XGBoost A machine-learning method based on an ensemble of boosted decision trees. It is widely used for classification, forecasting and risk scoring with structured business data because it can perform strongly while remaining comparatively practical to inspect and operate. ## Y ### YAML A human-readable format commonly used for configuration, pipelines and infrastructure definitions. In production systems, YAML often encodes how services, data jobs and AI workflows are deployed, so small mistakes can change behaviour as much as application code. ## Z ### Zero-shot learning The ability of a model to perform a task it was not explicitly trained on, using general knowledge and the instruction given at the time. Zero-shot behaviour is useful for speed, but it still needs evaluation against the organisation’s actual examples and success criteria. ### Zero trust A security model that assumes no user, device or service is trusted by default, even inside the organisation’s network. Access is granted through identity, least privilege and continuous verification, which is especially important when AI systems call tools or touch sensitive data. HTML: https://rodan.io/glossary