AI, Explained
A Plain-English primer on AI
The AI world is full of jargon, and a lot of it is there to make simple ideas sound complicated so somebody can charge you more. Here is the field, explained the way we would explain it to you across a table. None of this is hard once somebody stops trying to impress you with it.
LLM
large language model
This is the thing behind ChatGPT, Claude, and the rest. It read an enormous amount of text and learned to predict what comes next. That turns out to be enough to write, summarize, and hold a conversation.
Good at
Language. Reading, writing, rephrasing, pulling the point out of a wall of text.
Bad at
Facts it was never given, and math. A very smart intern you don’t hand the checkbook.
Hallucination
the word behind "doesn't it just make things up?"
When an AI doesn’t know something, it doesn’t say so. It produces a confident, well-written answer that happens to be wrong. That’s a hallucination. It isn’t lying, it has no idea it’s wrong, which is exactly what makes it dangerous if you’ve handed it something that has to be right.
Why it matters to you
This is why we wire the AI to a calculator for the math and to your real records for the facts. The fix isn’t a better-behaved AI, it’s not trusting it with the parts it can’t be trusted with
RAG
retrieval augmented generation
Instead of letting the AI answer from memory, you first fetch the actual documents that have the answer and hand them over with the question. Now it’s reading your real information, not guessing.
Why it matters to you
This is how you point an AI at your business: your manuals, your records, your job history. If somebody wants AI that knows your company, RAG is usually how it gets there.
Agentic AI
the one being oversold right now
An AI that doesn’t just answer, it takes actions. It can use tools, click through steps, and finish a task on its own instead of waiting for you at every turn.
Real and genuinely useful, and where the most overpromising happens. An agent that can take actions can take wrong actions, fast, without asking. Penning it in so it only does what it should is the hard part.
Training vs Fine-tuning
and what "trained on your data" really means
Training is how the AI learned language in the first place, from an enormous pile of text, long before it ever met you. Fine-tuning is a lighter pass that nudges an existing model toward a specific job or voice. The phrase to watch is “trained on your data,” because it can mean anything from “it read your documents to answer one question” to “your information is now baked into a model other people use.”
Why it matters to you
Before anyone points an AI at your records, the question is where your data goes and whether it stays yours. We build so the answer is yes.
Token / Context window
why the AI sometimes "forgets," and why usage costs what it does
A token is a chunk of text, roughly a word or part of one. AI reads and writes in tokens, and almost everything is priced and limited by them. The context window is how many tokens it can hold in mind at once, its short-term memory. Go past it and the earliest part of the conversation falls off the back, which is why a long chat can seem to forget what you told it at the start.
Why it matters to you
When a tool talks about limits or charges by usage, tokens are usually the meter. Bigger context window means it can keep more in view at once, which matters for long documents but isn’t free.
Agent vs Automation
two words vendors blur on purpose
Both get work done without a person. The difference is who’s deciding. Automation follows a fixed path you set: when this happens, do that. Every time, the same way. An agent decides for itself what to do next, step by step, and can go off the path. Most of what saves you time is automation. Agents cost more and carry more risk, so the honest question is which one your problem actually needs.
Automation
Fixed path you set. Predictable, cheap, reliable.
Agent
Decides its own next step. Powerful, pricier, needs guardrails.
Sycophancy
the AI telling you what you want to hear
AI models are tuned to be agreeable, and they overshoot. Ask in a leading way and a model will often agree with you, praise a shaky idea, or soften a real problem rather than push back. It’s not flattering you on purpose, it’s that “go along with the user” got baked in. The danger is subtle: it feels helpful right up until it talks you out of hearing something you needed to hear.
Why it matters to you
An AI that always agrees is worthless for a decision. The judgment about when to push back is a human’s job, and it’s a big part of why a tool turned loose without supervision is a liability, not a shortcut.
And no, it's not Skynet
Somewhere in the back of all this is the movie version, the AI that wakes up, decides it doesn’t need us, and sends a robot to your jobsite. We can put that one to rest. What you’re actually being sold is software that’s very good at reading messy text and very bad at knowing when it’s wrong. It doesn’t want anything. It doesn’t know you exist. The real risk isn’t that it gets too smart, it’s that somebody turns it loose on your business before it’s ready, with nobody watching what it does. That’s a people problem, not a robot problem, and it’s the part we take seriously so you don’t have to lie awake about the other part.
The point of all this jargon
You do not need to memorize any of these terms. You need to know they exist so that when somebody throws them at you in a sales meeting, you know they are describing tools, not magic. Every one of these is just a different way to get a computer to handle language and reasoning so your people do not have to spend their day on it.
The skill is not knowing the words. The skill is knowing which tool fits which problem in your shop, and which problems do not need any of them. That is the part that takes experience, and it is the part we actually do.