Prompt
What you type to the AI. That is it. A prompt is just your instructions, in normal words.
Why you care: Better instructions get better results. Most "AI is bad" moments are really "my prompt was vague."
Plain English The AI words,
The AI words,
un-jargoned.
Every one of these got explained to me in a way that made me feel dumber, not smarter. So here they are again, the way I actually understand them, sorted by what they are for. If a definition needs a second definition, it failed.
Using it day to day
Context
Everything you tell the AI about your situation before you ask for something. Your role, your goal, your constraints, your actual week.
Why you care: This is the whole game. AI with no context answers like a stranger. AI with your world answers like staff.
Context window
How much the AI can hold in its head at once. Go past it and the earliest things you said quietly fall out.
Why you care: On a long chat it can forget your first instructions. If it drifts, remind it, or start fresh and paste the essentials back in.
Chatbot
The chat box is the doorway, not the AI itself. You are talking through a window to the model behind it.
Why you care: Different chatbots can run the same model. What you are really choosing is the model and the setup around it.
Multimodal
AI that takes more than text. Show it a screenshot, a photo, or a voice note, do not just describe things in words.
Why you care: When explaining is slow, show it instead. A photo of the problem often beats a paragraph about the problem.
Memory
When a tool remembers you across separate chats, so you stop re-explaining who you are every time.
Why you care: Handy, but be deliberate about what it stores. I keep anything sensitive out of standing memory on purpose.
Iteration
Treat it like a conversation, not a vending machine. The second and third ask is usually where it gets good.
Why you care: The best output almost never comes from the first try. Push back, refine, correct. That back-and-forth is the work.
The words for how it works
Model
The specific AI brain you are talking to. Different models have different strengths, like different hires.
Why you care: You do not need to know how it works. You just need to know which one is good at your task. Try more than one.
LLM (large language model)
The kind of AI behind most of this. Think of it as an extremely well-read pattern machine.
Why you care: It is predicting likely words, not looking up facts. That is exactly why it can sound sure and still be wrong.
Machine learning
Software that learns patterns from examples instead of following rules a person typed out by hand.
Why you care: You will never do this yourself. It just explains why AI is confident about some things and clueless about others.
Token
How AI counts text, in chunks a bit smaller than words. It reads and writes in tokens.
Why you care: Mostly it matters for cost and length limits. You will rarely think about it, and that is fine.
Training data
Everything a model learned from. It shapes what it is great at and where it is quietly blind.
Why you care: If a model is weak on your niche, its training probably had little of it. Give it your own material to fix that.
Knowledge cutoff
The date a model's learning stopped. Ask it about last week and it may simply not know.
Why you care: For anything recent, give it the current facts or use a tool that can search. Do not trust its memory on the news.
Hallucination
When AI states something confidently that is just wrong. It is not lying. It is guessing and sounding sure.
Why you care: This is why you stay the editor. Never publish a number or a fact the AI gave you without checking it. Ever.
Building and connecting
Agent
A helper you set up once so it can do a job again and again, without you rebuilding it each time.
Why you care: One saved agent for reporting took a 10-hour job to 30 minutes. That is the difference between using AI and having AI work for you.
System prompt
The standing instructions that sit behind every conversation, setting the AI's job and rules once.
Why you care: This is where you save your voice, your do-nots, and your context so you stop re-typing them. The saved helper lives here.
Custom assistant (custom GPT)
A saved version of a chatbot with your instructions built in. A saved helper with a name.
Why you care: This is how you stop re-typing the same setup. Build it once, and your context rides along every time.
API
The plug that lets one piece of software talk to another. It is how AI gets wired into your other tools.
Why you care: You may never touch one directly, but it is the answer to "can this connect to that." Usually, yes.
Integration
Connecting AI to the tools you already use, so it works where you work instead of in a separate tab.
Why you care: The value jumps when AI meets your real data. A helper inside your inbox beats a smarter one you have to paste into.
MCP (Model Context Protocol)
A standard way to give AI safe access to your apps and files. Newer, but worth knowing the name.
Why you care: It is becoming the common plug for connecting AI to your stuff. You will hear it more, so now you know what it means.
Fine-tuning
Training a model deeper on your specific stuff. Heavy, technical, and almost never what you actually need.
Why you care: Skip it. Good context and a saved setup gets 95 percent of people 100 percent of the way. This is a rabbit hole.
RAG
A setup where the AI looks things up in your documents before answering, instead of guessing from memory.
Why you care: You will hear vendors sell this hard. Translation: "the AI can read your files first." Useful, not magic.
Trust, safety, and limits
Guardrails
The rules that keep an AI from doing things it should not, like sending, spending, or publishing on its own.
Why you care: My rule is simple: agents draft and route. A human approves. The governance is built in, not bolted on.
Human in the loop
A person who reviews and approves before anything ships. My entire governance rule, in three words.
Why you care: Every agent I run drafts and routes. None of them send, spend, or publish. A human approves. That is the whole safety model.
Bias
The lopsided assumptions a model picked up from its training. Worth watching, especially anything about people.
Why you care: Do not outsource judgment on hiring, money, or people to a tool that learned from the internet. Draft with it, you decide.
Prompt injection
A sneaky trick where hidden text tells an AI to ignore its rules. The reason you do not paste things blindly.
Why you care: If you point AI at a web page or a document you did not write, treat what it does next with a little suspicion.
Grounding
Making the AI answer from real sources you handed it, not its own memory. Fewer made-up facts.
Why you care: Give it the document and say "answer only from this." It is the simplest way to cut the confident nonsense.
Buzzwords to decode
Generative AI
The umbrella term for AI that makes new things: text, images, audio, code.
Why you care: When a headline says "generative AI," it just means the make-stuff kind. You are already using it.
AGI
The hypothetical someday-AI that could do anything a human can. Mostly a headline word, not a today word.
Why you care: Ignore the debate. Nothing about your Tuesday depends on whether it arrives in 2027 or never.
AI answer engine
The new search. Tools like ChatGPT, Claude, Perplexity, and Google AI, where people now ask instead of Googling.
Why you care: This is where people find answers now, so it is where brands get found. I work to be the source these engines cite.
Now you speak enough of it to be dangerous. The letter is where I turn the words into moves you can run this week.
Get the weekly letter