Artificial Intelligence (AI) and automation are transforming how we create content, manage data, and run online businesses.
This glossary explains the most common AI and automation terms in simple, practical language, so you can understand the tools, systems, and processes that save time and improve results.
A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z
A
AI (Artificial Intelligence)
Technology that enables computers to learn, reason, and make decisions like humans. Commonly used in content generation, chatbots, and automation tools.
Algorithm
A set of rules or steps an AI or computer program follows to solve a problem. Search engines, chatbots, and image generators all use algorithms.
API (Application Programming Interface)
Software that allows two systems to communicate. For example, using OpenAI’s API to connect ChatGPT to your own app or website.
Automation
The process of using software or tools to complete repetitive tasks automatically, such as posting content, sending reports, or updating data.
Artificial general intelligence (AGI)
A theoretical form of AI that can perform any intellectual task that a human can. Current systems, like ChatGPT, are not AGI but narrow AI.
Auto-scheduling
Automatically planning or queuing posts, emails, or workflows using tools like Buffer, Zapier, or Make.
B
Bot
A piece of software designed to perform automated tasks. Examples include chatbots, data crawlers, and customer service assistants.
Bias (AI bias)
When AI systems produce unfair or inaccurate results due to biased data or training. Developers work to reduce this by improving datasets and transparency.
Big data
Extremely large sets of information that can be analysed by AI to identify trends, patterns, and predictions.
Benchmarking
Testing an AI model’s performance against a standard dataset or goal to measure quality or speed.
C
Chatbot
An AI-powered assistant that interacts with users via text or voice, such as ChatGPT or customer service bots on websites.
Cloud computing
Using remote servers hosted on the internet to store and process data instead of relying on local computers.
Computer vision
AI that allows computers to “see” and interpret images or videos — used in facial recognition, self-driving cars, and image generators.
Context window
The amount of text or data an AI model can process at one time. Larger context windows mean the AI can handle longer documents or conversations.
Custom GPT
A version of ChatGPT trained or configured for a specific task, such as writing blog posts, generating code, or analysing SEO data.
D
Data scraping
Collecting information from websites or databases automatically. Useful for market research or competitor analysis, but must be done ethically.
Deep learning
A type of AI that mimics how the human brain processes information, using neural networks to recognise patterns in large datasets.
Dataset
The collection of information an AI model is trained on. Better datasets produce smarter, more accurate models.
Decision tree
A visual or logical model that helps AI make structured decisions based on input data.
Diffusion model
A type of AI model used for image generation (like DALL·E or Midjourney) that gradually transforms random noise into a realistic picture.
E
Embedding
Converting words or phrases into numbers so AI models can understand relationships between them. Essential for search, recommendations, and chatbots.
Ethical AI
Developing and using AI responsibly — ensuring fairness, transparency, and privacy protection.
Edge computing
Processing data close to where it’s generated (like on a user’s device) instead of on a remote server, to reduce delay and increase privacy.
F
Fine-tuning
Training an existing AI model on new, specific data to make it perform better in a narrow field, such as legal or medical writing.
Flow automation
Connecting tools and tasks into an automatic sequence — for example, sending new form submissions straight to a CRM and follow-up email.
Framework
The foundation or structure developers use to build AI and automation systems, such as TensorFlow or PyTorch.
G
Generative AI
AI that creates new content — such as text, images, videos, or code — rather than simply analysing existing data.
GPT (Generative Pre-trained Transformer)
The type of AI model that powers ChatGPT. It’s trained on vast amounts of text to predict and generate human-like language.
Grounding
The process of keeping an AI system accurate and factual by connecting it to trusted data sources.
H
Hallucination
When AI generates false or made-up information that sounds convincing. It happens when the model guesses without enough real data.
Hybrid automation
Combining human decision-making with automated workflows. Humans oversee the process while automation handles repetitive steps.
I
Inference
When an AI model applies what it has learned to make predictions or generate output in real time.
Integration
Connecting multiple apps or tools so they work together automatically — for example, linking Google Sheets to email notifications.
Intent recognition
AI’s ability to understand what a user means, not just what they say. Commonly used in chatbots and search engines.
IoT (Internet of Things)
Everyday objects that connect to the internet and share data, like smart thermostats, fridges, or security cameras.
J
JSON
A lightweight data format often used for transferring information between systems in AI and automation workflows.
K
Knowledge base
A collection of information an AI system can draw from to provide accurate answers or recommendations.
Keyword extraction
Using AI to identify the most important words or topics in a piece of text. Helpful for SEO and summarisation.
L
Large language model (LLM)
A type of AI trained on vast amounts of text to generate or understand natural language. ChatGPT and Gemini are examples.
Low-code automation
Automation tools that let non-developers build workflows using drag-and-drop interfaces instead of complex coding.
Labelled data
Data that has been categorised or tagged to help train machine learning models. For example, labelling images of cats and dogs.
M
Machine learning (ML)
A subset of AI where systems learn and improve from data without being explicitly programmed for every task.
Model training
The process of feeding data into an AI model so it can learn patterns, relationships, and outcomes.
Model drift
When an AI model becomes less accurate over time because real-world data changes. Regular retraining helps fix this.
Multi-agent system
AI systems made up of multiple “agents” that cooperate or compete to solve tasks more efficiently.
N
Neural network
A type of AI inspired by the human brain, made up of layers of nodes (or “neurons”) that process and learn from data.
Natural language processing (NLP)
The field of AI that helps computers understand and respond to human language — used in chatbots, translation, and sentiment analysis.
Node
A point in a workflow or neural network that processes information or performs an action.
Normalization
Standardising data so it’s consistent and comparable across different sources, which improves model accuracy.
O
OpenAI
The company behind ChatGPT, DALL·E, and other generative AI tools.
Optical character recognition (OCR)
Technology that converts scanned images or handwritten text into editable digital text.
Output token
A chunk of text generated by an AI model. The more tokens produced, the longer the response (and the more processing used).
P
Parameter
A variable within an AI model that influences how it processes and predicts data. The more parameters, the more complex the model.
Pipeline
A series of automated steps in a workflow, from data collection to processing to output.
Prompt
The input you give an AI model — such as a question, command, or instruction. The better the prompt, the better the result.
Prompt engineering
The skill of designing clear, effective prompts to get accurate and relevant responses from AI models.
Predictive analytics
Using AI to analyse patterns in data and forecast future outcomes, such as customer behaviour or sales trends.
Q
Quantisation
The process of compressing AI models to make them faster and more efficient without losing too much accuracy.
R
Reinforcement learning
Training an AI model by rewarding correct behaviour and penalising mistakes, similar to how humans learn from experience.
Retrieval-augmented generation (RAG)
A method that lets AI pull in real, up-to-date information from a database or the web before answering questions.
Robotic process automation (RPA)
Software that mimics human actions on computers — like copying data, clicking buttons, or filling out forms.
S
Sentiment analysis
AI that detects whether written content expresses positive, negative, or neutral emotions. Often used for brand monitoring or reviews.
Speech recognition
Technology that converts spoken language into text, used in voice assistants and transcription tools.
Supervised learning
Machine learning trained with labelled data, where the model learns from examples with known outcomes.
Synapse
The connection between nodes in a neural network that transfers information and strengthens with training.
T
Token
The smallest unit of text an AI model processes. Words and punctuation are broken down into tokens to help AI understand meaning.
Transformer model
The architecture behind modern large language models like GPT and BERT. It allows AI to handle context and relationships efficiently.
Training data
The information used to teach an AI model how to respond or make predictions. Quality and variety are crucial for reliable results.
Trigger
An event that starts an automation — for example, receiving an email, submitting a form, or adding a new spreadsheet row.
U
Unsupervised learning
When AI learns patterns from unlabelled data without explicit instructions — often used for clustering or discovery.
Upscaling
Using AI to improve image or video quality by increasing resolution without losing detail.
Use case
A specific way AI or automation is applied to solve a real-world problem, such as writing content, analysing SEO data, or sending reports.
V
Vector database
A database designed to store and search embeddings (numerical representations of data). Essential for chatbots and semantic search.
Voice synthesis
AI that generates realistic human speech from text. Used in virtual assistants, audiobooks, and videos.
Versioning
Keeping track of different versions of an AI model or automation workflow to manage updates safely.
W
Workflow automation
Connecting different tools so actions happen automatically — for example, creating Trello cards from form submissions.
Webhook
A simple way for one app to send data to another automatically when something happens — for example, when a lead form is filled in.
Weight
A value inside a neural network that determines how strongly one input influences the next. Adjusted during training to improve accuracy.
X
XML feed
A data file used to transfer information, often between e-commerce platforms or automation tools.
Y
Yield rate
In automation, the percentage of successful runs compared to failed ones. High yield means your workflow is running efficiently.
Z
Zero-shot learning
When an AI system performs a task it’s never been explicitly trained on, using general knowledge to make sense of new information.