AI Glossary: The Terms Every Company Needs to Understand
AI vocabulary has turned into an alphabet soup that gets in the way more than it helps when it's time to decide. This glossary translates the terms that show up most in proposals and technical conversations into plain language, focused on what each one means in practice for your company, no buzzwords, no academic lecture. Use it as a quick reference whenever a vendor throws out an acronym.
The fundamentals
AI (Artificial Intelligence): an umbrella term for systems that perform tasks that used to require a human: understanding text, classifying, deciding, generating content. In a business context, what matters is where it moves a number in the operation. See AI for companies.
Generative AI: the type of AI that creates new content (text, image, code) instead of just classifying existing data. It's the technology behind assistants that converse and produce.
LLM (Large Language Model): the "engine" behind text generative AI. It's a model trained on massive volumes of text that predicts and generates language. When someone mentions GPT, Gemini, or Claude, they're talking about LLMs.
Agents and automation
AI agent: software that perceives context, decides the next step, and executes an action inside your systems (schedules, logs, qualifies). Unlike a chatbot, it resolves things instead of just answering. Details in what is an AI agent.
Chatbot: a scripted conversation program (menus and fixed answers). Good for simple questions; gets stuck once the person strays from the script. It's the step before the agent.
Multi-agent architecture: instead of one robot trying to handle everything, several specialist agents with a "router" that directs each request to the right one. It's what allows scaling service while keeping quality.
Handoff: passing service from the AI to a human, with the full context of the conversation, when the case calls for judgment. A good handoff is what gives you the confidence to put an agent on the front line.
Knowledge and data
RAG (Retrieval-Augmented Generation): a technique where the AI first searches for the relevant excerpts in your database and only then generates the answer based on them. It's what keeps the answer anchored in your data and reduces made-up content. Foundation of corporate search with AI.
Knowledge base: the set of documents, policies, and company data organized for the AI to consult. It's what makes the agent sound like your company, not like a generic AI.
Hallucination: when the AI answers with something that sounds right but is made up. It's mitigated with RAG and traceability (the answer points to its source), never trusting it in the dark.
Fine-tuning: fine adjustment of a model with your own examples to specialize it for a task or tone. Useful in specific cases; RAG often already solves it without the cost of fine-tuning.
Development and project
MVP (Minimum Viable Product): the highest-impact slice that already delivers value and can reach the user early. Starting with an MVP spreads out the investment and validates hypotheses with real use. More in how much an app costs.
Custom-built: software built for your operation, connected to your systems and your rules. The opposite of a generic template/no-code tool. Costs more upfront and has no platform ceiling.
Integration: connecting the AI to the systems that run the business (ERP, CRM, WhatsApp, calendar). It's what takes the AI out of the role of just answering questions and puts it to work executing.
API: the "outlet" through which two systems talk to each other. When someone talks about integrating AI with your ERP, it's almost always via API.
Frequently asked questions
What's the difference between generative AI and an LLM?
Generative AI is the category of AI that creates new content. An LLM (large language model) is the type of model behind text generative AI, the engine that predicts and generates language. Every LLM is generative AI, but there's also generative AI for image, audio, and code.
What is RAG in simple terms?
It's the technique where the AI first searches for the relevant information inside your database and only then writes the answer based on it, citing the source. This keeps the answer anchored in your data and reduces the risk of the AI making things up.
What's the difference between a chatbot and an AI agent?
A chatbot follows a script of menus and fixed answers and gets lost outside the script. An AI agent understands natural language, keeps context, and executes real actions in your systems, resolving the case end to end.
Do I need fine-tuning to use AI in my company?
In most cases, no. Most business needs are solved with RAG (connecting the AI to your knowledge base), which is faster and cheaper than training a model. Fine-tuning comes into play for specific cases of tone or a very particular task.
16 years building custom software; leads NexUnio's AI engineering. Learn about NexUnio · LinkedIn