LLM — Stands for "Large Language Model." It's the brain behind AI chatbots. Imagine someone who has read every book, website, and article ever written and can answer almost any question — that's basically what this is, except it's a computer program.

Example: "My son told me the AI I've been using runs on an LLM — basically a really well-read robot brain."

Large Language Model — Same thing as LLM, just the full name instead of the abbreviation.

Example: "When the news talks about 'large language models,' they just mean the technology that makes AI chatbots smart."

AI Model — The "brain" of any AI tool. It's a computer program that learned patterns from tons of information, so now it can do things like answer questions or create images.

Example: "Think of the AI model like a recipe — it was trained on a bunch of ingredients (data) and now it can cook up answers."

Chatbot — A computer program you can have a conversation with by typing, just like texting a friend. Older chatbots were stiff and robotic. Newer ones sound almost human.

Example: "That little chat bubble on the bottom of a website that says 'How can I help you?' — that's a chatbot."

Generative AI — AI that makes brand new stuff — writing, pictures, videos, music. Instead of just searching for things that already exist, it creates things that never existed before.

Example: "I told the AI to write me a birthday poem for my granddaughter and it made one up on the spot — that's generative AI."

Prompt — Whatever you type or say to the AI to tell it what you want. It's basically your instructions or your question.

Example: "I typed 'explain how to make sourdough bread in simple steps' — that whole sentence is the prompt."

Prompt Engineering — The art of writing really good instructions for the AI so you get better answers. The more specific you are, the better results you get.

Example: "Instead of just saying 'write me an email,' prompt engineering is saying 'write me a short, friendly email to remind my tenant that rent is due Friday.'"

ChatGPT — The AI chatbot that started all the buzz in late 2022. You type something, it types back. Made by a company called OpenAI. It's the one everyone and their neighbor has heard of.

Example: "My coworker asked ChatGPT to help plan a Thanksgiving menu and it came up with recipes, a shopping list, and a cooking timeline."

Claude — Another AI chatbot, made by a company called Anthropic. Known for being helpful with long documents and detailed tasks.

Example: "I copied my entire lease agreement into Claude and asked 'what are my responsibilities as a landlord?' and it broke it all down for me."

Gemini — Google's AI chatbot. It's built into a lot of Google stuff like Gmail and Search.

Example: "Now when I search something on Google, Gemini sometimes gives me a written answer right at the top instead of just links."

GPT — The technology behind ChatGPT. If ChatGPT is the car, GPT is the engine under the hood.

Example: "When someone says 'GPT-5.2,' they're talking about the latest version of that engine — it's smarter than the older ones."

Copilot — Microsoft's AI helper that lives inside Word, Excel, Outlook, and other Microsoft programs. It helps you write, do math, and make presentations.

Example: "I opened Excel, clicked Copilot, and told it 'make a chart of my monthly expenses' and it did it in seconds."

OpenAI — The company that made ChatGPT. One of the biggest names in AI right now. Microsoft gave them a ton of money.

Example: "Every time you hear about ChatGPT in the news, OpenAI is the company behind it."

Anthropic — The company that made Claude. Started by people who used to work at OpenAI. They focus a lot on making AI safe and trustworthy.

Example: "Anthropic's whole thing is making sure AI helps people without doing anything harmful."

Google DeepMind — Google's big AI research lab. They made headlines years ago by building AI that beat world champions at really hard games, and now they build Gemini.

Example: "Google DeepMind is where a lot of the behind-the-scenes AI breakthroughs happen."

Meta AI — The AI team at Meta (the company that owns Facebook and Instagram). They give away a lot of their AI for free so anyone can use it.

Example: "Meta AI released their AI models for free, which is why so many smaller AI apps exist now."

Perplexity — A search engine powered by AI. Instead of giving you a list of links like Google, it reads the internet for you and gives you a direct, clear answer with sources.

Example: "I asked Perplexity 'what's the best way to remove red wine from a white tablecloth?' and it just told me the answer — no clicking through 10 websites."

Sora — OpenAI's AI that makes videos from words. You describe a scene, and it creates a realistic-looking video clip out of thin air.

Example: "Someone typed 'a golden retriever running on a beach at sunset' into Sora and it made a gorgeous video of exactly that."

Stable Diffusion — A free AI tool that creates pictures from written descriptions. Unlike some others, you can download it and run it on your own computer.

Example: "My nephew uses Stable Diffusion to make cool artwork just by describing what he wants to see."

Veo3 — Google's AI that creates videos from text descriptions, similar to Sora.

Example: "Google showed off Veo3 making a realistic cooking video just from a written description of the dish being prepared."

Twin.so — A no-code platform that lets you build AI agents in plain English to automate business tasks autonomously. Launched January 27, 2026.

Example: "I used Twin.so to build an AI agent that handles customer follow-ups — I just described what I wanted and it built it for me."

Grok — xAI's (Elon Musk's company) AI chatbot built into X (formerly Twitter). It has real-time access to what's happening on X right now.

Example: "I asked Grok 'what's trending in AI today?' and it pulled the latest conversations from X to give me an answer."

Machine Learning — The basic idea that computers can learn from examples instead of being told exactly what to do. Show it enough examples of spam emails, and it figures out how to spot them on its own.

Example: "Machine learning is how Netflix knows what shows to recommend to you — it learned from what you've watched before."

Deep Learning — A fancier version of machine learning that can handle more complicated stuff like understanding photos and human speech. It uses layers of processing, kind of like how our brains work.

Example: "Deep learning is the reason your phone can look at a photo and tell you there's a dog in it."

Neural Network — The structure inside AI that's loosely based on how our brain is wired. It's layers of tiny decision-makers that pass information along until they reach an answer.

Example: "Think of a neural network like a chain of people whispering a message — each person adds a little understanding until the final person has the full picture."

Natural Language Processing — The part of AI that understands and responds in normal human language. It's why you can talk to AI like you're talking to a person, not a machine.

Example: "Natural language processing is why you can ask your phone 'What's the weather like today?' in plain English and it understands you."

Training Data — All the information that was fed into the AI so it could learn. Books, websites, articles, images — whatever the AI "studied" before it was ready to answer your questions.

Example: "It's like how a student studies textbooks before an exam — the textbooks are the training data."

Token — The tiny pieces that AI breaks your words into when it reads them. A token might be a whole word, part of a word, or just a punctuation mark. This is how AI counts and processes text.

Example: "The word 'grandmother' is one token, but a long sentence might be 15-20 tokens. AI tools use tokens to measure how much text they can handle."

Token Limit — The maximum number of tokens an AI can handle in one go — both what you send it AND what it sends back. Once you hit this limit, the AI can't take any more input or give more output.

Example: "I tried to paste a huge document into ChatGPT but hit the token limit — it told me the text was too long to process all at once."

Parameters — The internal "settings" or "knobs" inside an AI model. During training, these get adjusted millions of times until the AI gets good at its job. More parameters usually means a smarter AI.

Example: "When they say a model has '100 billion parameters,' just think of it as having 100 billion tiny dials that were fine-tuned during training."

Context Window — How much text the AI can "see" and remember during your conversation. Think of it as the AI's short-term memory or the size of its desk.

Example: "Claude has a big context window, so I could paste in a really long document and it still remembered the beginning when I asked about the end."

Context Limit — The maximum size of that context window. Once you go over it, the AI starts forgetting the earlier parts of your conversation.

Example: "After chatting with the AI for a really long time, it forgot what we talked about at the start — I hit the context limit."

Inference — The moment when the AI actually reads your question and thinks up an answer. Training is when it learns; inference is when it uses what it learned.

Example: "Every time you press 'send' on a message to ChatGPT and it responds, that's inference happening."

Temperature (in AI) — A setting that controls how creative or predictable the AI's responses are. Low temperature = safe and straightforward. High temperature = more creative and unexpected.

Example: "For a factual business report, you'd want low temperature. For brainstorming fun party themes, you'd want high temperature."

Fine-tuning — Taking an existing AI and giving it extra training on specific information so it gets better at a particular job or sounds more like your brand.

Example: "We fine-tuned the AI on our company's past emails so now its responses sound like they came from one of our team members."

Grounding — Making sure the AI bases its answers on real, actual facts instead of just making stuff up. Grounded AI checks real sources before answering.

Example: "Our grounded AI assistant looks up the real prices in our store system before telling customers how much something costs."

RAG (Retrieval Augmented Generation) — A way to make AI smarter by having it search through specific documents first, then use what it finds to give you a better answer. The AI does its homework before responding.

Example: "We set up RAG so when employees ask the AI about company policies, it actually reads our employee handbook before answering."

Artificial Narrow Intelligence — What ALL current AI actually is. It's really good at one thing (or a few things), but it can't truly think or understand like a human. Every AI tool you use today is this.

Example: "The AI that can beat anyone at chess still can't help you pick out an outfit. That's narrow intelligence."

AGI (Artificial General Intelligence) — A future AI that could think and learn like a human and do anything a person can do. This doesn't exist yet — it's what companies are racing to build.

Example: "AGI would be like having an AI that could be a doctor in the morning, a lawyer in the afternoon, and a chef at night — we're not there yet."

Superintelligence — An imagined future AI that would be way smarter than every human on Earth combined. This is still science fiction.

Example: "Superintelligence is what movies like Terminator are about — a computer that's smarter than all of us. We're nowhere close to this."

Predictive AI — AI that looks at past patterns to guess what will happen next. This has been around much longer than chatbots.

Example: "When Amazon says 'you might also like this product' — that's predictive AI guessing what you'll want to buy next."

Autonomous AI — AI that makes decisions and takes actions on its own without asking a human for permission each time.

Example: "An autonomous AI on a stock trading platform might buy and sell investments all day long without a human approving each trade."

Agentic AI — The newest big thing in AI. This is AI that can plan out steps, use different tools, and complete complicated tasks on its own — almost like having a virtual assistant that actually does the work, not just answers questions.

Example: "Agentic AI could look at your calendar, find a good time for a dentist appointment, call the dentist's booking system, and schedule it — all on its own."

AI Agent — A specific AI helper that actually does tasks for you. It can plan, make decisions, use apps, and work through problems step by step, like a really capable virtual employee.

Example: "I set up an AI agent that reads my incoming emails every morning, sorts them into categories, and drafts replies for me to review."

Multimodal — AI that can understand more than just text. It can also look at pictures, listen to audio, and watch videos, all at once.

Example: "I took a picture of a rash on my arm and asked the multimodal AI what it might be — it looked at the photo and gave me some possibilities."

Computer Vision — AI's ability to "see" and make sense of pictures and videos, the way our eyes and brain work together.

Example: "Computer vision is how your phone's camera can point at a flower and tell you what kind of plant it is."

Text-to-Image — AI that draws or creates a picture based on what you type. Describe something and it makes it.

Example: "I typed 'a fluffy cat wearing a top hat sitting in a teacup' and the AI drew it for me in 10 seconds."

Text-to-Video — Same idea, but it creates a video clip instead of a still picture. This is newer and still getting better.

Example: "A business owner typed a description of their product being used and the AI made a short video ad from it."

Deepfake — A fake video or audio clip made by AI that looks and sounds scarily real. It can make it look like someone said or did something they never actually did.

Example: "Scammers made a deepfake video of a company's CEO telling employees to wire money — it looked completely real but was 100% fake."

Voice Cloning — AI that can copy someone's voice from just a short recording, then make that voice say anything.

Example: "My friend got a phone call that sounded exactly like her daughter asking for money — but it was actually AI voice cloning used by a scammer."

Synthetic Media — A fancy term for any content (pictures, videos, audio, writing) that was created by AI instead of a real person.

Example: "That perfect-looking model in the online ad? She's not real — she's synthetic media made entirely by AI."

AI Watermark — A hidden tag or stamp placed inside AI-created content so people can identify it was made by AI and not a real person.

Example: "Google puts AI watermarks on images their AI generates so you can check if a photo is real or AI-made."

API — Stands for "Application Programming Interface." It's like a power outlet that lets one program plug into another. Businesses use APIs to add AI features into their own apps and websites.

Example: "Our web developer used the Claude API to add an AI chat helper directly into our company website."

AI Wrapper — A product that looks shiny and new, but underneath it's just using someone else's AI (like ChatGPT) with a pretty cover on top. The core brain isn't theirs.

Example: "That $30/month AI writing tool? It's basically an AI wrapper — just ChatGPT with a nicer-looking screen and some templates."

AI-Powered Search — Search engines that use AI to give you real answers in plain language, not just a bunch of links you have to click through.

Example: "Instead of scrolling through 10 websites, AI-powered search just told me 'The best stain remover for red wine on cotton is…' in one clear answer."

Open Source AI — AI that's given away for free so anyone in the world can use it, change it, and build with it. Think of it like a free recipe that anyone can cook and modify.

Example: "Because Meta released their AI as open source, small companies can build their own AI tools without paying huge fees."

Cloud AI — AI that runs on big computers somewhere far away (the cloud) instead of on your own device. Most AI you use today works this way.

Example: "When you use ChatGPT on your phone, your phone isn't doing the hard work — it's sending your question to powerful computers in the cloud."

Edge AI — AI that runs right on your own device (your phone, your camera, your appliance) instead of needing the internet. It's faster and more private.

Example: "Your phone's face unlock uses edge AI — it recognizes your face right on the phone without sending your photo anywhere."

Automation — Using technology to do tasks that people used to do by hand. AI-powered automation is smarter and more flexible than the old-school kind.

Example: "We automated our appointment reminders — now AI sends personalized texts to every client the day before, without anyone having to do it manually."

Robotics — Physical machines (robots) that can do tasks in the real world. When you add AI to robots, they get much smarter about handling new situations.

Example: "The factory down the road just got AI-powered robots that can pack different-sized boxes without being reprogrammed each time."

Hallucination — When AI confidently tells you something that is completely made up. It sounds totally real and believable, but it's fiction. This is one of AI's biggest problems right now.

Example: "I asked the AI for a good Italian restaurant nearby and it gave me a name, address, and phone number — but the restaurant doesn't exist. That's a hallucination."

Bias (in AI) — When AI gives unfair or lopsided results because the information it learned from was unbalanced. Like a judge who only heard one side of the story.

Example: "An AI hiring tool kept recommending mostly men because it was trained on resumes from a company that had historically hired mostly men."

Guardrails — Safety fences built into AI so it won't say or do harmful things. They're the rules that keep AI from going off the rails.

Example: "When you ask an AI chatbot how to do something dangerous and it refuses — those are the guardrails working."

Alignment — Making sure AI actually does what humans want it to do and shares our values. Think of it like training a really smart dog — you want it to be helpful, not chaotic.

Example: "Alignment is about making sure that as AI gets smarter, it still wants to help us and not go rogue."

AI Safety — Everything related to making sure AI doesn't cause harm — whether by accident, by misuse, or by doing things we didn't expect.

Example: "Anthropic was started specifically because its founders believe AI safety should come first."

Responsible AI — Building and using AI the right way — being fair, honest, and thinking about how it affects real people.

Example: "Our responsible AI policy says a human must always review AI-generated content before it goes to customers."

AI Ethics — The big moral questions about AI. Should AI make hiring decisions? Who's responsible when AI makes a mistake? Is it fair to train AI on people's creative work?

Example: "AI ethics asks questions like: if an AI gives wrong medical advice and someone gets hurt, who's to blame?"

AI Regulation — Laws and rules that governments are creating to control how AI is built and used, since it's all so new.

Example: "Europe was one of the first to pass AI regulation — they now classify AI tools by how risky they are and have rules for each level."

AI Copyright — The messy legal debate about who owns AI-created content, and whether it's okay for AI to learn from copyrighted books, art, and music.

Example: "Artists are suing AI companies over AI copyright, saying their paintings were used to train AI without permission."

Turing Test — A test from 1950: if you're chatting with something and can't tell if it's a human or a computer, the computer passes the test. Modern AI is getting really close.

Example: "Sometimes when I'm chatting with ChatGPT, I honestly forget it's not a real person — that's basically passing the Turing test."

Singularity — The scary/exciting idea that one day AI will become so smart it changes everything about civilization in ways we can't predict. Nobody knows if or when this will happen.

Example: "The singularity is what tech people argue about at parties — some say it's coming in 10 years, others say it'll never happen."

AI Bubble — The worry that everyone is way too hyped about AI right now, and that companies are spending billions without knowing if they'll ever make that money back — like the internet craze of the late 1990s.

Example: "My financial advisor mentioned the AI bubble — he said to be careful about investing too much in AI companies that have big promises but no profits yet."