The AI
dictionary,
in English.
49 AI terms explained the way a smart friend would, no jargon, no textbook definitions. Bookmark it, share it with the person who keeps asking what an LLM is.
A
- Agent / agentic AI
- An AI that can take multiple steps on its own toward a goal, like searching the web, running code, or using other tools, instead of just answering one message and stopping.
- AI (Artificial Intelligence)
- The big umbrella category. Any computer system trained to recognize patterns and make predictions, not a robot brain and not magic.
- API
- A way for one piece of software to talk to another. In AI, it's what lets an app or a piece of code send a request straight to a model and get an answer back, with no chat window involved.
- Artifacts
- A Claude feature that opens a separate window for things like documents, code, or visuals it creates, so they don't get buried in the chat and you can keep working on them.
- Automation (vs AI)
- Automation follows fixed rules and breaks when something unexpected happens. AI adapts to input it's never seen before.
B
- Bias (AI bias)
- When an AI's answers unfairly favor or disfavor something, usually because of patterns baked into its training data. Different problem than hallucination: hallucination is about being wrong, bias is about being unfairly skewed.
C
- Chatbot
- Any program you talk to through a chat window. Not every chatbot runs on an LLM, older chatbots just followed scripted rules.
- ChatGPT vs LLM (the app vs the engine)
- ChatGPT is the app (the interface you type into). GPT-4 (or whichever model) is the LLM underneath it doing the actual work. Same idea with Claude: Claude is the app, the model underneath is the LLM.
- Claude Code
- Anthropic's command-line tool that lets Claude read, write, and run code directly in your own project files, not just chat about code.
- Claude Project
- A dedicated workspace in Claude that remembers your context (goals, files, running history) across sessions, so you don't have to re-explain yourself every time.
- CLI (command-line interface)
- A text-only way to control a program by typing commands instead of clicking buttons. It's the plain screen full of text that looks like the 'terminal' or that old-school black window. Claude Code runs in a CLI. Looks intimidating at first, but once it clicks you can move faster than clicking around ever would.
- Context (in a prompt)
- The background info you give an AI: who you are, what you're trying to do, what your constraints are. The difference between a generic answer and a personal one.
- Context window
- The total amount of text (measured in tokens) an AI can hold in its memory at once during a conversation, including everything you've typed plus everything it's replied. Analogy: a whiteboard of a fixed size. Once it's full, writing more means erasing the oldest lines at the top.
- Cybersecurity
- The practice of protecting computers, networks, and accounts from people who shouldn't have access to them, everything from a strong password to how a company defends against hackers.
D
- Database
- Organized storage for information a program needs to keep track of, like your account details, past conversations, or a company's customer list. Analogy: a giant, searchable filing cabinet a computer can search through in a fraction of a second.
E
- Embeddings
- A way of turning words, images, or other data into lists of numbers that capture their meaning, so a computer can measure how similar two things are. It's what makes search and RAG possible behind the scenes.
F
- Fable
- One of Claude's models, part of the same family as Sonnet and Opus, each built with a different balance of speed, cost, and capability.
- Fine-tuning
- Taking an already-trained AI model and training it further on a smaller, focused set of examples so it gets better at one specific kind of task. Analogy: a general doctor doing a specialty residency to become a surgeon.
- Folder
- A container for organizing files on a computer, exactly like a physical folder for organizing papers. Nothing fancy, just a way to group related things together so they're easy to find later.
- Foundation model
- A large, general-purpose AI model trained on broad data that can be adapted for many different tasks, rather than built to do just one job.
G
- Generative AI
- AI that creates new content, like text, images, or audio, rather than just analyzing or sorting existing data.
- Guardrails
- The built-in limits and safety rules that keep an AI from doing or saying things it shouldn't.
H
- Haiku
- Claude's fastest and most affordable model, built for simple, high-volume tasks where speed matters more than deep reasoning.
- Hallucination
- When an AI states something wrong with total confidence. Every AI tool does it sometimes. Doesn't make AI useless, just means anything that actually matters needs a quick check.
I
- Inference
- The moment an already-trained AI actually generates a response to your prompt. Training happens once, ahead of time. Inference happens every single time you use it.
J
- Jailbreak
- An attempt to trick an AI into ignoring its safety guidelines. Worth knowing the term exists, not something to try.
- JSON (JavaScript Object Notation)
- No, not a guy named Jason. JSON is just a tidy way to write down information so both people and computers can read it. It pairs labels with values, like "name": "Izic" or "followers": 4, wrapped in curly braces. It's how apps and AI tools quietly pass data back and forth behind the scenes: your settings, an API's answer, a list of results. If you've ever peeked under the hood of an app and seen text full of curly braces and quotes, that was JSON. Analogy: a fill-in-the-blank form where every blank is clearly labeled, so nothing gets mixed up.
L
- Latency
- How long it takes an AI to respond after you send a message.
- LLM (Large Language Model)
- A specific type of AI trained on huge amounts of text so it can understand what you say and write back in plain, natural language. Every LLM is AI, but not every AI is an LLM (same as every car is a vehicle, but not every vehicle is a car).
M
- Machine Learning (ML)
- A method inside AI where systems learn from data instead of being explicitly programmed with rules.
- Markdown (.md file)
- A plain text file format that uses simple symbols, like # for a heading or * for a bullet point, to add structure without needing a full word processor. Easy for both humans and AI to read and write, which is why AI tools often store notes and instructions in .md files behind the scenes.
- MCP (Model Context Protocol)
- A standard that lets AI models like Claude connect to outside tools and data sources (Canva, Gmail, a database) in a consistent way, instead of every connection being custom-built.
- Memory (AI memory via folders and files)
- How some AI tools remember things across separate conversations, since each conversation normally starts fresh. Instead of holding everything in its head, the AI saves notes to files (often Markdown files) inside folders, then reads those files back in at the start of a new conversation. Analogy: leaving yourself sticky notes in labeled folders, so future-you doesn't have to start from scratch every time.
- Model size (small vs large)
- Bigger models generally know more and reason better, but are slower and more expensive to run. Smaller models are faster and cheaper but less capable. Bigger isn't automatically better, it depends on the task.
- Multimodal
- An AI that can work with more than just text, for example reading images, listening to audio, or understanding video, not just typing back and forth.
N
- Neural network
- The general design most modern AI is built on, loosely inspired by how neurons in a brain connect and pass signals to each other. It's the underlying structure, not the AI itself.
O
- Opus
- Claude's most capable model, built for the hardest, highest-stakes tasks. Slower and pricier than Sonnet, but reasons more deeply and handles more complex work.
P
- Parameters
- The internal settings inside an AI model that get adjusted during training. More parameters generally means a bigger, more capable model.
- Prompt
- Simply the message you type to an AI. That's it, no more complicated than that.
- Prompt engineering
- The skill of writing clear, specific prompts to get better results from an AI. Basically, getting good at asking the right way.
R
- RAG (retrieval-augmented generation)
- A technique where an AI looks up relevant information from an outside source, like your documents or the internet, before answering, so it can respond with current, specific facts instead of relying only on what it memorized during training.
S
- Server
- A computer (or program running on one) that sits somewhere else and responds when your device asks it for something, like a webpage, a file, or an AI's answer. Analogy: a waiter who takes your order to the kitchen and brings back what you asked for.
- Sonnet
- One of Claude's models, built as a strong all-around balance of speed, cost, and capability. Good for most everyday tasks without the wait or price tag of the largest model.
- System prompt
- Instructions given to an AI behind the scenes, before you type anything, that shape how it behaves for the whole conversation. Different from a regular prompt, which is what you type.
T
- Temperature (AI setting)
- A setting that controls how random or predictable an AI's answers are. Low temperature gives safer, more repetitive answers. High temperature gives more creative, surprising ones.
- Token / tokens
- The small chunks of text an AI reads and writes one at a time, roughly a piece of a word rather than a whole word. It's the actual unit AI companies use to measure how much text a conversation costs or how much it can handle at once. A single word often splits into more than one token: "hamburger" typically breaks down into three, ham, bur, and ger.
- Tool use
- When an AI can call outside tools mid-conversation, like running a web search or a calculation, instead of only using what it already knows.
- Training data
- The huge collection of text, images, and other material an AI model learned from before it was ever released. It's where the AI's knowledge comes from, and also where its blind spots and biases come from.
Z
- Zero-shot / Few-shot
- Zero-shot means asking an AI to do a task with no examples given. Few-shot means giving it a couple of examples first to show exactly what you want.
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