The industry loves a word that makes simple things sound complicated. You should not have to learn a new language to decide whether AI is worth your money. Here are the terms you will actually hear, explained the way I would explain them to a friend over a coffee, with what each one means for your business underneath.
A job role given to AI, not a chatbot. It has a goal, the tools it needs to reach it, and clear limits. You give it a task and it gets on with it. We build agents the way you would hire someone: a clear remit, the right access, and a manager checking the important work.
The "brain" doing the thinking. LLM stands for large language model, the kind behind ChatGPT and similar. There are big expensive ones and smaller cheaper ones; the skill is picking the right size for the job rather than always reaching for the biggest.
Getting the work to move between your tools without you copying and pasting it by hand. A lead arrives, gets logged, gets researched, gets a follow-up drafted, all on its own. The unglamorous stuff that quietly gives you your week back.
Simply the instruction you give the AI. A good prompt is the difference between a useless answer and a useful one, which is most of what "prompt engineering" actually means underneath the fancy name.
AI that can operate software the way a person does, by looking at the screen and clicking. It matters when your work is trapped in an old system that will not connect to anything. The AI does the clicking; you approve the result.
The same idea, but specifically driving a web browser, to gather information or prepare work inside web portals and dashboards. Useful for the jobs that live in someone else's website all day.
AI that runs on your own computers instead of sending your data off to an outside service. The choice you make when the information is sensitive and you want it to stay in the building.
A way of running AI so the powerful machine sleeps until there is real work, then wakes, does it, and goes quiet again. Lower bills, less wasted power. We care about this one a lot.
Retrieval-augmented generation. It means giving the AI your own documents to answer from, so it works off your facts instead of guessing from general knowledge. The plain version: "we let it read your stuff before it answers."
How much the AI can hold in mind at once for a single task. Bigger is not always better, stuffing too much in costs more and can make answers worse, so part of the craft is feeding it only what matters.
When an AI states something confidently that is simply wrong. It is real, it happens, and pretending otherwise is how people get burned. The fix is design: checking, sources, and a human on anything that matters.
Training a model a little further on your specific material so it sounds and behaves more like you need. Often it is not necessary, and a simpler approach gets you most of the way for far less money.
Plenty of people in this industry use the jargon precisely because it makes simple things sound expensive and clever. My rule is the opposite: if I cannot explain what I am building to you in plain English, I do not understand it well enough to charge you for it.
You should always know, in normal words, what a system does, what it is allowed to touch, and where a human stays in control. That is not too much to ask. It is the whole point.
You do not need to know any of these words to work with us. That is our job. You just need to know where your business is losing time.