Glossary
AI, in plain English.
No jargon, just clear definitions of the terms you'll hear in any AI conversation, so you can follow along, ask sharper questions, and never feel sold to.
The terms
Every term you need, none of the jargon.
Each definition is written for a business owner, not an engineer, and ends with why the term actually matters to your operation.
Artificial intelligence
Software that can perform tasks that normally need human judgment, reading, writing, recognizing patterns, making decisions from data. Why it matters: it's the umbrella term for everything else on this page, and the reason to care is simple, it can take work off your team's plate.
Machine learning
A way of building AI where the software learns patterns from examples instead of being told exact rules for every situation. Why it matters: it's why AI gets better with more data, and why the data you already collect is valuable.
Large language model (LLM)
An AI model trained on huge amounts of text so it can read, write and answer in natural language. Why it matters: it's the engine behind most chatbots, drafting tools, and "ask a question, get an answer" features you'll be offered.
Generative AI
AI that creates new content, text, images, audio, code, rather than just analyzing or sorting what already exists. Why it matters: it's what lets a system draft an email, summarize a contract, or write first-pass copy for you.
Prompt
The instruction or question you give an AI system, in plain language. Why it matters: better prompts get better results, and a well-designed system hides most of that complexity from your team.
Token
A small chunk of text, roughly a word or part of a word, that AI models read and generate one piece at a time. Why it matters: usage costs and speed are usually priced by tokens, so it's worth knowing the term when you see a bill or a usage limit.
Hallucination
When an AI system states something confidently that isn't true or isn't backed by real data. Why it matters: it's the single biggest reason AI needs guardrails and human review before it touches customers, contracts or money.
Guardrails
Rules and checks built around an AI system to keep it inside safe, approved boundaries, what it can say, access, or do. Why it matters: guardrails are what make AI safe to put in front of customers or staff, not an afterthought.
RAG (retrieval-augmented generation)
A method where an AI looks up your actual documents or data before answering, instead of relying only on what it was trained on. Why it matters: it's how you get an AI assistant that knows your policies, prices and products instead of guessing.
Vector database
A specialized way of storing information so an AI can quickly find the most relevant document or answer by meaning, not just by matching keywords. Why it matters: it's the piece that makes RAG and smart search over your own files possible.
Fine-tuning
Additional training that adjusts an existing AI model on your specific examples, so it picks up your tone, terminology or judgment calls. Why it matters: it's one route to an AI system that sounds like your business, not a generic assistant.
Inference
The moment an AI model actually produces an answer, as opposed to when it was trained. Why it matters: this is the step that costs money and takes time every time someone uses the system, which is why response speed and running cost matter in planning.
AI agent
An AI system that can take multi-step action, not just answer a question, checking a database, sending an email, updating a record, usually within limits you set. Why it matters: this is where AI moves from "answers questions" to "gets work done."
Agentic workflow
A sequence of steps where one or more AI agents hand work to each other, and sometimes to people, to complete a full process. Why it matters: it's how a single request, like "onboard this client", can trigger a chain of actions instead of a chain of manual tasks.
Workflow / orchestration
The layer that decides what happens in what order, who, or what, does each step, and what to do when something goes wrong. Why it matters: good orchestration is what keeps an automated process reliable instead of fragile.
Computer vision
AI that reads and interprets images or video, spotting objects, defects, text or patterns a person would otherwise have to look at. Why it matters: it's behind quality inspection, document scanning, and anything that starts with a photo.
Natural language processing (NLP)
The branch of AI focused on understanding and working with human language, written or spoken. Why it matters: it's what powers chatbots, email triage, sentiment analysis and search that understands what you meant, not just what you typed.
Predictive analytics
Using historical data to estimate what's likely to happen next, demand, churn, delays, defaults. Why it matters: it turns your past records into an early warning system instead of a filing cabinet.
Anomaly detection
AI that learns what "normal" looks like in your data so it can flag the transaction, reading or event that isn't. Why it matters: it catches fraud, errors and equipment problems earlier than a person scanning reports would.
Training data
The examples an AI model learns from before it's put to work. Why it matters: results are only as good as this data, so "what data do we have, and is it clean" is often the real first question in any AI project.
API / integration
The technical connection that lets one piece of software, an AI tool, talk to another, your CRM, your calendar, your accounting system. Why it matters: an AI tool that can't connect to your existing systems usually means more manual work, not less.
Human in the loop
A design choice where a person reviews or approves an AI's output before it goes out the door, instead of the system acting fully on its own. Why it matters: it's a practical way to get AI's speed while keeping a person accountable for anything that matters.
Proof of concept
A small, working version of an AI solution, built and tested on your real data, before you commit to the full build. Why it matters: it lets you see the return before you spend the full budget, which is exactly how we structure engagements.
ROI (return on investment)
The value an AI project returns compared to what it cost in time, money and effort. Why it matters: it's the number that should drive every decision here, not the technology for its own sake.
You don't need to know any of this to work with us, we translate your goals into plain English on every call. But the more of these terms feel familiar, the sharper the questions you can ask, and the easier it is to spot the difference between a real opportunity and a sales pitch.
Have a question this page didn't answer? Check the FAQ , or see these terms in action on our use cases page.
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