AI, explained for everyone
Pick who you are. We will explain AI, LLMs, and common terms at the level that actually makes sense for you.
What is AI, really?
Who's reading this?
Big ideas in small words. No confusing stuff.
What is AI?
AI is like a robot made of words. It read millions of books and websites, so it knows a lot. When you ask it a question, it uses everything it learned to give you an answer. It is not really thinking the way you are, but it is very good at finding the right words.
What is an LLM?
LLM is the computer-science name for a word robot. "Large" means it learned from a really big pile of writing. "Language" means it works with words and sentences. "Model" means it is a pattern it learned. All the AI helpers you can talk to (like Claude or ChatGPT) use an LLM as their brain. The LLM is the brain; the helper is the tool built around it.
How does it learn?
The AI read so many things that it learned which words go together. It is like how you learned what sounds letters make by seeing them over and over. Nobody told it every rule. It figured them out by reading enough examples. Once it is done learning, it uses those patterns to answer your questions.
What can you do with it?
You can ask it questions, have it help you write a story, explain something confusing, or figure out a hard problem. Grown-ups use it for work stuff too, like writing emails or building websites.
What is AI?
AI tools like the ones you probably use (ChatGPT, Claude, Gemini) are large language models: systems trained on massive amounts of text that can write, answer questions, summarize documents, and generate code. They are useful but not magic. The quality of what you get back depends a lot on how you ask and how things are configured.
What is an LLM?
An LLM (Large Language Model) is the architecture behind most AI assistants today: a neural network trained to understand and generate text. What makes them useful is not that they "know" things in a human sense. It is that pattern-matching learned from training on enormous text datasets generalizes well to new tasks. Most AI tools you use day-to-day (ChatGPT, Claude, Gemini) are built on top of LLMs. The product is the interface; the LLM is the model underneath.
How does it learn?
LLMs are trained in two main phases. Pre-training: the model reads massive amounts of text and learns statistical patterns in language. Fine-tuning: the model is then refined on curated examples and human feedback to make it helpful and safe. After training is done, the model's weights are frozen. It does not continue learning from your conversations.
What can you do with it?
Drafting and editing writing, summarizing long documents, answering questions, generating code, brainstorming, translating, explaining complex topics, data analysis. The useful mental model: a capable but fallible collaborator, best used for tasks where you can review the output, not for tasks where errors are invisible and costly.
What is AI?
AI is software that can read, write, and reason. For a business owner, the practical question is not how it works but what it can do for you: write product descriptions, draft customer emails in your voice, summarize a contract, create captions for your posts, research a competitor. It is available any time, does not call in sick, and costs a fraction of hiring someone for the same tasks.
What is an LLM?
LLM is the technical name for the kind of AI that talks with you in sentences. You do not need to know how it works, only that it is what runs under the hood of tools like Claude, ChatGPT, and Gemini. When someone says "language model," they mean an AI you can have a conversation with.
How does it learn?
Before you ever used it, the AI was trained on a huge amount of writing: books, articles, websites. That training is done. When you talk to it, it is using what it already knows, not updating in real time from your conversation. However, many AI tools use past conversations to improve future versions by default unless you opt out. Check the privacy settings of whichever tool you use.
What can you do with it?
Try it for: customer emails in your voice, product listings, contract drafts, marketing brainstorms, social media captions, FAQ content, or thinking through a pricing decision. Start with one task you do repeatedly. If the AI saves you 30 minutes a week on it, it is already worth using. Start free at claude.ai.
What is AI?
For practical purposes: a system that takes text in and produces text out, with enough training behind it to be useful for a wide range of tasks. The important properties for engineers are the behavioral ones. It has no persistent state between sessions. It does not learn from your inputs. Output quality depends heavily on what you put in the context window. And within a given model family, prompt engineering often matters more than model selection.
What is an LLM?
A model trained to predict the next token given a sequence of prior tokens, scaled up until it generalizes. The training happens in two phases: first on a huge corpus of text (this builds general knowledge and language ability), then on curated examples with human feedback to shape it toward helpful, safe behavior. After that, weights are fixed. The model you talk to is a static artifact. It cannot update itself from your conversation.
How does it learn?
It does not learn during use. All learning happened before deployment. Pre-training built the knowledge and language capability. A second phase (fine-tuning) shaped the behavior: how it follows instructions, how it declines requests, what tone it uses. The fine-tuning does not add new world knowledge, it just changes how the model behaves. After that the weights are frozen, and what you see is what you get.
What can you use it for?
Code generation, review, and refactoring. Writing and running tests. Documentation. Research synthesis. Agentic automation with tool use. The practical ceiling is context window size and how reliably the model catches its own errors, not the breadth of tasks it can attempt.
What is AI?
AI tools take input (a prompt, a document, a codebase) and generate useful output. They are typically non-deterministic. The same input can produce different results depending on sampling settings (a parameter called temperature). That makes them powerful and unpredictable in equal measure. Good configuration is what narrows the unpredictability into something useful and consistent.
What is an LLM?
An LLM (Large Language Model) is the model underneath the AI tools you use today. ChatGPT, Claude, and Gemini are products built on top of LLMs. The LLM is the actual trained model; the product is the interface around it. For product teams, what matters is the behavioral characteristics: LLMs are non-deterministic, can hallucinate confidently, have knowledge cutoffs, and perform very differently depending on how they're prompted. Designing around those properties is the real challenge.
How does it learn?
LLMs are trained before you use them, then fine-tuned to behave helpfully and safely. The training is done once per model version. It does not learn from your individual conversations. This matters for product design: you are not training the model through usage, you are composing a prompt that shapes its behavior within a session. Good system prompts act like product specs.
What can you use it for?
Synthesizing user research, generating draft copy, brainstorming feature names, writing acceptance criteria, explaining technical concepts in plain language, reviewing designs for accessibility or consistency, generating test cases. Most effective as a structured collaborator: give the AI specific context and a defined output format, then review and iterate. Treat it as a fast first-draft generator that needs an editor, not an oracle.
What is AI?
AI stands for "artificial intelligence." The simplest way to think of it: someone fed an enormous amount of written text to a computer, and the computer found patterns in that text. Now when you type a question, the computer uses those patterns to write back an answer. It does not understand your question the way a person would. But it has seen so much writing that its answers are often very helpful. It is a tool, like a calculator or a search engine, one that works with whole sentences and ideas.
What is an LLM?
LLM stands for "Large Language Model." That is the technical name for the kind of AI that talks with you in sentences. You do not need to know what those words mean to use it. When you see it mentioned, it points to the same thing: an AI assistant that works with text. Claude, ChatGPT, and Gemini are all products built on top of LLMs. The LLM is the brain, and those are the tools built around it that you actually talk to. They work the same basic way: trained on a lot of text, and they use what they learned to have a conversation with you.
How does it learn?
Before you ever used it, the AI was trained on a huge amount of writing: books, articles, websites. It went through all of that and found patterns in how words and ideas fit together. When training was done, those patterns were locked in. When you ask it a question now, it draws on what it learned then. It is not updating its knowledge in real time from your conversation. However, many tools use past conversations to improve future versions by default. You usually have to opt out in settings. Check the privacy settings of whichever tool you use.
What can you do with it?
Ask it questions, have it explain something in plain language, help you write or improve a piece of writing, draft an email or letter, summarize a long document, or think through a problem together. Start with something small, something you would normally spend ten minutes figuring out. Ask the AI, see what comes back, and adjust from there. The best way to learn what it can do is to try it on a real task you actually have.
Common AI terms
The same words, explained at the level that works for you. These match your persona selection above.
Prompt
What you type to the AI. Like asking a question, but to a computer.
Model
The AI's brain. It learned from reading millions of things.
Token
A tiny piece of a word. The AI breaks everything into tiny pieces to read it. "Hello" might be one token.
Context Window
How many pieces the AI can hold in its memory at once (like short-term memory). If you talk too long, it forgets the beginning.
System Prompt
A secret set of rules someone gives the AI before you start talking. You usually cannot see it. It is like a teacher giving the AI instructions before class.
Agent
An AI that can do things by itself (like clicking buttons or sending messages), not only answer questions.
Fine-tuning
Teaching an AI to get really good at one specific thing, like cooking or math, by giving it lots of examples about that topic.
RAG
Giving an AI a book to look things up in while it talks to you, so it knows about things it wasn't taught originally.
API
A special door that lets one computer program talk to another. It's how apps use AI without you seeing how it works behind the scenes.
Prompt
The input you send to an AI: a question, a task, or structured instructions. Better prompts get better results; it's worth learning what "better" means for your tool.
Model
The trained AI system itself. Different models (like Claude Sonnet vs. Opus) have different capability and cost trade-offs. Knowing which model you're using matters.
Token
The unit AI models use to process text, roughly ¾ of a word. Models have context limits in tokens. Longer conversations cost more tokens and eventually hit a ceiling.
Context Window
Everything the AI can "see" in a given conversation, including previous messages. Models range from ~128k up to 1M tokens in the largest. When full, earlier parts get cut off or summarized.
System Prompt
Instructions injected before the conversation starts. Tools use these to define the AI's behavior, rules, and persona. You usually don't see it, but it shapes how the AI responds.
Agent
An AI that takes actions, not only generates text. An agentic AI can call tools, run code, browse the web, or trigger workflows. Claude Code is an example.
Fine-tuning
Adjusting an already-trained model on specific data to improve performance in a narrow domain. Makes sense when prompt engineering isn't enough. Requires data and infrastructure to do well.
RAG
Retrieval-Augmented Generation. Rather than relying only on training data, RAG pulls relevant documents at query time and includes them in the prompt. Common for knowledge-base Q&A.
API
The interface through which you call AI models programmatically. Most AI tools expose an API so developers can integrate AI into their apps.
Prompt
What you type into the AI. The more specific you are, the better the answer. Instead of "write a customer email," try describing exactly who it's for and what it should say.
Model
The version of the AI you're talking to. Think of it like tiers of a service. A more powerful model may give better answers but cost a bit more. For everyday tasks, the standard model is usually fine.
Token
The way the AI measures conversation length. You don't need to worry about this much. It's why very long conversations can cost more or hit a limit.
Context Window
How much the AI can hold in one conversation before it starts forgetting earlier parts. Think of it like a whiteboard: once it's full, something has to be erased. Keep conversations focused.
System Prompt
Background instructions given to the AI before your conversation starts. You usually don't see them. They're how a tool shapes the AI's behavior. A customer service chatbot uses one to stay on-topic.
Agent
An AI that can take actions on your behalf rather than only answering questions, like booking an appointment, sending an email, or updating inventory. These are newer and more powerful.
Fine-tuning
Adjusting an already-trained AI to be especially good at your specific type of business or content. Usually done by larger companies. For most small businesses, the off-the-shelf tools give plenty of value without this.
RAG
A way of giving the AI access to your specific documents or knowledge so it can answer questions using your real information (like a chatbot that knows your exact products and policies). Accuracy depends on retrieving the right document, so always verify important answers.
API
The technical connection that lets one piece of software talk to another. Relevant if you want to integrate AI into your website or booking system. Usually something a developer sets up.
Prompt
The input passed to the model at inference time: user message, tool schemas, results, prior turns, injected context. Output quality is primarily a function of prompt quality.
Model
A specific checkpoint with fixed weights. "Claude Sonnet 4.6" refers to a specific trained artifact. Selection involves capability, context window, cost-per-token, and rate limit trade-offs.
Token
The unit models use to process text (roughly ¾ of an English word on average, varying by language and content). Token count determines cost and whether your input fits in the context window.
Context Window
Full input sequence visible at inference: system prompt, conversation history, tool results, retrieved docs. Frontier models range from ~128k up to 1M tokens. Long-context performance can degrade for content in the middle of very long inputs, a documented tendency and not a universal guarantee.
System Prompt
Privileged instruction prefix injected before the user turn. In Claude Code, composed from global CLAUDE.md, project CLAUDE.md, active skills, and hook injections. Defines persona, constraints, available tools, and workflow rules.
Agent
A model in an agentic loop with tool use: perceive state → plan → act → observe → repeat. Key concerns: tool schema quality, error handling, context consumption per iteration, and human escape hatches.
Fine-tuning
Continued training on curated data to shift model behavior or specialize it for a domain. Use when prompt engineering and retrieval have both hit their ceiling. Requires data, compute, and a real evaluation setup. Not worth the overhead until you have exhausted cheaper approaches.
RAG
Retrieval-Augmented Generation. Augmenting inference context with documents retrieved from an external store via semantic similarity or keyword search. Trade-off: relevant context vs. prompt size, retrieval latency, chunking quality.
API
HTTP endpoint (or SDK wrapper) for model inference. Key parameters: model, messages array, system prompt, max_tokens, temperature, tool definitions. Batching, streaming, and tool use are first-class features. Rate limits and cost tracking matter at scale.
Prompt
What a user types or sends to an AI. In product design, prompts are where user intent meets AI capability. Designing AI-powered features often means designing how the user prompts: structured inputs, suggested starters, or open fields.
Model
The AI system itself. Different models have different strengths, speeds, and costs. For product decisions: fast and cheap for high-volume low-stakes tasks, slower and more capable for complex reasoning.
Token
How AI models measure the length of text they process. Longer content costs more and every model has a limit. Designing around token limits means thinking carefully about what context the AI actually needs.
Context Window
Everything the AI can see at once in a session: the system prompt, conversation history, any documents fed to it. Think of it as working memory. What goes into the context window is a product decision as much as a technical one.
System Prompt
Hidden instructions that shape how the AI behaves before a user sees it. This is where product teams define the AI's role, tone, constraints, and capabilities. A well-designed system prompt is like a job description for the AI.
Agent
An AI that takes actions, not only generates text. For product thinking, an agent changes the design question from "what should the AI say?" to "what should the AI do, and when should it ask a human first?"
Fine-tuning
Customizing a model's behavior by training it on specific data. Relevant when the base model doesn't behave right in a specific domain or tone. Usually a later-stage investment, not a starting point.
RAG
A technique for grounding AI responses in specific content: documentation, knowledge bases, product catalogs. Instead of relying on training data, RAG retrieves relevant content and puts it into context. Useful when accuracy and currency matter.
API
The programmatic interface to an AI model. For product teams, the API is what makes AI a platform: developers can call it from any surface. API design and rate limits affect what's feasible to build and at what scale.
Prompt
What you type to the AI. The more clearly you explain what you need, the better the answer tends to be. If the first answer isn't quite right, try saying the same thing differently.
Model
The specific version of the AI you are talking to. Different models are better or worse at different tasks. You usually do not have to choose. The tool picks for you. If you see a choice, a description is usually nearby to help.
Token
A small piece of text that the AI uses to measure how much it is processing. You do not need to know how to count tokens. Know that very long conversations can sometimes hit a limit. If that happens, starting a new conversation usually helps.
Context Window
How much of the current conversation the AI can hold in mind at once. It is like short-term memory. If a conversation goes on long enough, the AI may lose track of things said early on. Keeping conversations focused on one topic at a time helps.
System Prompt
Instructions given to the AI before you ever type anything. You typically do not see them. They are set up by the tool or the person who built the product you are using. They shape how the AI behaves and what it will or will not do.
Agent
An AI that takes actions, not only answers questions. Instead of saying "here is how to send that email," an agent AI can send the email itself. These are newer and more powerful. They usually ask you to confirm before doing something important.
Fine-tuning
Teaching an AI to be especially good at something specific by training it on extra examples. You would not do this yourself. It is something AI companies or larger businesses do to make a tool behave well in a particular area.
RAG
A way of giving the AI access to specific documents or information so it can answer questions using that material, though it depends on finding the right document, so answers are not guaranteed to be perfect. An example: a customer support tool that knows your exact policies, not only general knowledge.
API
A technical connection that lets different software programs talk to each other. You will not interact with it directly as a user. It is what lets developers add AI features to apps and tools you already use.
Tools for building with AI
Research, templates, and configs for each, built up over time.
Claude Code
Anthropic's agentic CLI. Settings, hooks, subagents, skills, and a maintenance loop to keep it current.
Available nowGitHub Copilot
GitHub's AI coding assistant. Research and config templates coming.
Coming soonGemini Code Assist
Google's AI coding tool. Research and config templates coming.
Coming soonChatGPT / Codex
OpenAI's models for coding. Research and config templates coming.
Coming soon