AI is moving quickly and the language around it is moving just as fast. One week you’re hearing about ‘LLMs’ and ‘prompting’, the next it’s ‘RAG’ and ‘multimodal’… If you’re a business leader or marketer trying to make smart decisions, that can be frustrating, especially when different people use the same term to refer to different things.

Our AI glossary is designed to make AI terminology clearer. It may not turn you into a machine learning engineer, but it will help you ask better questions, evaluate vendors more confidently and avoid misunderstandings when AI projects move from ‘interesting idea’ to real implementation.

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Why using the correct AI terminology matters

Knowing the right AI terms could help you better business decisions, faster. When teams use AI language accurately, it becomes easier to define requirements, compare tools, set realistic expectations and measure impact. It can also reduce risk.

A lot of AI disappointment comes from terminology confusion – for example, assuming ‘AI automation’ means a system can run unsupervised or assuming ‘trained on your data’ means your documents are private and fully understood.

Using the correct terminology could help you:

  • Avoid costly misalignment between leadership, marketing, IT and suppliers
  • Spot inflated claims and ask sharper follow-up questions
  • Choose the right approach for the job, whether that’s analytics, automation, content support, customer service or internal knowledge
  • Plan governance properly, especially around privacy, compliance and brand risk
  • Improve outcomes by understanding what matters, including data quality, evaluation, human review, security and change management

Here are some of the key AI terms you’re increasingly likely to encounter.

AI glossary

AI (artificial intelligence)

A broad term for computer systems that perform tasks typically associated with human intelligence, such as language understanding, pattern recognition, decision support and content generation.

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AI agent

An AI system that can work towards a goal by deciding what steps to take and carrying them out, often using tools such as calendars, CRMs, web search or databases. Some agents can complete several steps with limited human input.

Algorithm

A set of instructions a computer follows to solve a problem or complete a task. Algorithms are used throughout computing, including in machine learning.

API (application programming interface)

A way for software systems to communicate and exchange data. Many AI products use APIs to integrate with websites, CRMs, help desks and internal systems.

ASR (automatic speech recognition)

Technology that converts spoken audio into text. This is particularly useful for call transcription, meeting notes and voice assistants.

Bias (model bias)

When an AI system consistently produces unfair, inaccurate or unbalanced results because of factors such as its training data, design or how it is used. Bias can affect fairness, accuracy and business risk.

Chatbot

A conversational interface that responds to user questions. Chatbots may be rule-based or AI-driven, for example by using an LLM.

Classification

A machine learning task in which a system assigns data to categories, for example spam or not spam, positive or negative sentiment, or high-intent versus low-intent leads.

Computer vision

AI and related technologies used to interpret images and video, such as identifying objects, analysing visual information or checking product imagery for compliance.

Context window

The amount of information an AI model can work with at one time, including your prompt, earlier conversation and other information you provide. A larger context window means you can give the model more information, but it may still miss or misuse details buried in a long input.

Dataset

A collection of data used to train, fine-tune or evaluate an AI model. In business, datasets could include support tickets, product catalogues, analytics or CRM records.

Embeddings

A way of turning text or other information into numbers so an AI system can compare meaning and find similar content. Embeddings are commonly used in semantic search and RAG.

Fine-tuning

Further training an existing AI model on additional examples so it performs better for a particular task, subject area or style. Fine-tuning changes the model itself, while RAG gives it relevant outside information when it produces a response.

Generative AI

AI systems designed to generate new content, such as text, images, video, code or audio.

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Guardrails

Controls designed to reduce risk in AI systems, such as content filters, brand rules, restrictions on tools or actions and escalation to human review.

Hallucination

When a generative AI system produces information that sounds plausible but is incorrect, fabricated or unsupported by the available evidence.

Human-in-the-loop (HITL)

An approach in which people review, approve, correct or supervise AI outputs or actions. This can be particularly important for sensitive, regulated or customer-facing work.

Inference

The stage where a trained AI model is actually used to produce an answer, prediction or other result. Training is when the model learns; inference is when you use it.

LLM (large language model)

A type of AI model trained on large amounts of data to understand and generate language. LLMs power many tools used for writing, answering questions and summarising information, and can also sit behind AI agents and multimodal systems.

Machine learning (ML)

A branch of AI in which systems learn patterns from data rather than being explicitly programmed with fixed rules for every possible situation.

Model

A system produced through training that can perform tasks such as predicting outcomes, classifying information or generating content.

Multimodal AI

AI that can process or generate more than one type of information, or modality, such as text, images, audio and video.

Natural language processing (NLP)

A field of AI and computer science concerned with processing, understanding and generating human language. It is used for tasks such as sentiment analysis, summarisation and information extraction.

Neural network

A type of machine learning model made up of many connected parts that work together to recognise patterns. Neural networks underpin many modern AI systems used for language, images and predictions.

Overfitting

When a model performs well on the data it was trained on but performs poorly on new data, usually because it has learned the training examples too specifically rather than generalising effectively.

Parameter

A value the model learns during training that helps shape how it processes information and produces answers. Modern AI models can have huge numbers of parameters, although having more does not automatically make a model better.

Prompt

The instruction or input given to an AI model. Prompts can include context, constraints, tone guidance and examples to help shape the output.

Prompt engineering

The practice of designing and refining prompts to make AI outputs more useful, relevant and consistent for a particular task.

RAG (retrieval-augmented generation)

A technique that lets an AI system look up relevant information from sources such as your documents, databases or knowledge base before generating an answer. This can make responses more accurate, relevant and up to date, but it doesn’t guarantee they will always be correct.

Reinforcement learning (RL)

A way of training AI using feedback or rewards to encourage better behaviour. Reinforcement learning is used in the development of some generative AI models.

Sentiment analysis

A technique for identifying sentiment expressed in text, such as whether it is positive, negative or neutral. It is commonly used for social listening, reviews and customer-support analysis.

Supervised learning

A machine learning approach in which a model learns from labelled examples, for example emails that have already been classified as spam or leads that are known to have converted.

Temperature

A model setting, where supported, that affects how much variation there is in generated outputs. Higher temperatures generally make responses more varied, while lower temperatures tend to make them more predictable.

Token

A unit of text processed by a language model. A token may be a whole word, part of a word, punctuation or another small piece of text. Usage limits and context windows are often measured in tokens. API usage is also commonly priced according to the number of input and output tokens processed.

Training

The process through which a model learns patterns from data. Training is different from using a model to produce an output, known as inference, and from giving it external information at the time of a request through approaches such as RAG.

Vector database

A database designed to store embeddings and quickly find information with a similar meaning. Vector databases are often used for semantic search and RAG systems.

Workflow automation

Using software to carry out repeatable processes with limited manual intervention. When combined with AI, automation can support tasks such as triaging enquiries, drafting responses or generating reports.

Explore what AI could mean for your business visibility

If you’re looking at AI through a marketing lens, particularly how it can support online visibility, content production, research or more consistent execution, Submerge can help.

Our team brings together specialists across AI visibility, search, content and web development. Combined with technical SEO, useful and authoritative content marketing and effective web design, these disciplines can help strengthen how your business is found, understood and experienced online.

Book a free consultation with the Submerge team and we can help you find practical AI opportunities for your business and build an approach that supports your wider visibility and growth goals.