Plain-English definitions for UX, product, user research, accessibility, service design, CRO, AI, and digital strategy terms used in day-to-day digital work.
Showing 42 of 388 terms
Artificial Intelligence
Artificial Intelligence is the use of machines and systems to perform tasks that typically require human intelligence, such as learning, reasoning, and problem-solving.
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Machine Learning is a subset of AI that enables systems to learn from data and improve performance without being explicitly programmed.
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A model is a system or representation used to process data and generate outputs, often trained to perform specific tasks.
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An algorithm is a set of rules or instructions used to solve a problem or perform a task.
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A Large Language Model is an AI model trained on vast amounts of text data to understand and generate human-like language.
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Generative AI refers to systems that create new content such as text, images, or code based on learned patterns from data.
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A prompt is the input or instruction given to an AI system to guide its output or response.
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Prompt engineering is the practice of designing and refining prompts to produce better, more reliable outputs from AI systems.
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Automation is the use of technology to perform tasks with minimal human intervention.
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Data is raw information collected and stored for analysis, processing, or decision-making.
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A data pipeline is a system that moves, processes, and transforms data from one source to another.
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A data source is the origin from which data is collected or accessed.
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Training data is the dataset used to teach a machine learning model how to perform a task.
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A dataset is a structured collection of data used for analysis, training models, or processing.
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Data quality refers to the accuracy, completeness, consistency, and reliability of data.
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Model accuracy measures how often a model produces correct or expected outputs compared to known outcomes.
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Model drift occurs when a model’s performance declines over time due to changes in data or real-world conditions.
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Bias in AI refers to systematic errors or unfair outcomes caused by skewed data, assumptions, or model design.
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Hallucination in AI refers to when a model generates incorrect or fabricated information that appears plausible.
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Fine-tuning is the process of further training a pre-trained model on specific data to improve performance for a particular task.
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RAG is a technique that combines information retrieval with generative AI, allowing models to use external data to produce more accurate and context-aware responses.
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Embeddings are numerical representations of data, such as text or images, that capture meaning and relationships.
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A vector database stores and retrieves data based on vector representations, enabling similarity search and retrieval.
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Human-in-the-Loop is a process where human input is used to review, validate, or guide automated systems.
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Inference is the process of using a trained model to generate outputs or make predictions based on new input data.
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Model output is the result or response generated by a model after processing input data.
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AI output refers to any result generated by an AI system, including text, images, predictions, or decisions.
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AI reliability refers to the consistency and dependability of AI outputs over time and across different scenarios.
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AI trust refers to the level of confidence users have in an AI system’s outputs, behaviour, and decision-making.
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AI governance is the framework of policies, processes, and controls used to manage and oversee AI systems responsibly.
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AI ethics involves the principles and guidelines that ensure AI systems are developed and used in a fair, transparent, and responsible way.
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Data labelling is the process of tagging data with meaningful labels so it can be used for training machine learning models.
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Data processing is the transformation of raw data into a usable format through cleaning, structuring, and analysis.
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Data integration is the process of combining data from multiple sources into a unified view.
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Data enrichment is the process of enhancing existing data by adding additional information or context.
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How accurate, useful, and relevant a result is.
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Signals are data points or triggers that indicate changes in user behaviour, context, or external factors.
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GEO is the process of optimising content to be surfaced, cited, or used by AI systems such as large language models and generative search experiences.
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An entity is a clearly defined concept, object, or thing that can be understood independently, such as a person, place, organisation, or idea.
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A knowledge graph is a structured representation of entities and their relationships, used by search engines and AI systems to understand and connect information.
View glossary termArtificial Intelligence · Strategy
Structured data is a standardised format used to organise and label content so it can be easily understood by search engines and AI systems.
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Information retrieval is the process of finding relevant information from large datasets based on a query or input.
View glossary termLET'S WORK TOGETHER
UX, research and product leadership for teams tackling complex digital services. The work usually starts where things have become harder than they need to be: unclear journeys, inconsistent products, competing priorities, or teams trying to move forward without a clear direction. I help simplify the problem, shape the right next step, and turn complexity into something people can actually use.
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