Plain-English definitions of the terms you actually need to know — no PhD required.
The AI landscape can feel confusing because companies, models, and products all have different names. Here's the map.
| Company | Models | Consumer Product | Best For |
|---|---|---|---|
| OpenAI | GPT-4o, GPT-4.5, o1, o3 | ChatGPT | General use, largest plugin ecosystem, image generation (DALL-E) |
| Anthropic | Claude Opus, Sonnet, Haiku | Claude.ai | Long documents, nuanced writing, complex instructions, Claude Projects |
| Gemini 2.0, 2.5 Pro | Gemini | Google Workspace integration, multimodal (text + image + video) | |
| xAI | Grok 3 | Grok (in X) | Real-time social media data, X platform integration |
| Meta | Llama 3, 4 | Open-source (no app) | Self-hosting, data privacy, high-volume use cases, no API fees |
The core technology behind ChatGPT, Claude, and Gemini. An LLM is software trained on massive amounts of text that can understand and generate human language. Think of it as a very sophisticated autocomplete — it predicts what should come next based on patterns in the data it was trained on.
Why it matters for your business: When someone says “AI,” they usually mean an LLM. Understanding this helps you evaluate what AI can and can’t do for your business.
Software that can take actions on its own — not just answer questions, but actually do work like sending emails, updating spreadsheets, or researching leads. An agent uses an LLM as its “brain” but connects to tools and systems to execute tasks.
Why it matters for your business: This is where business value lives. Chatbots answer questions; agents do work. The cohort members who get the biggest ROI are the ones building agents, not just using chat.
The instructions you give an AI. Can be a simple question or a detailed, multi-paragraph brief. The quality of the output depends heavily on the quality of the input — which is why prompt engineering is its own skill.
Why it matters for your business: Good prompting is the cheapest, fastest way to improve your AI results. Before you build anything complex, get your prompts right.
The unit AI uses to process text. Roughly ¾ of a word. When providers talk about “context windows” or pricing, they measure in tokens. GPT-4o can handle about 128,000 tokens (~96,000 words) in a single conversation.
Why it matters for your business: Token limits determine how much information you can give the AI at once. Token usage determines your cost. Both matter for production systems.
When an AI generates information that sounds confident and plausible but is factually wrong. The AI isn’t “lying” — it’s generating the most statistically likely next words, which sometimes means fabricating facts, citations, or data.
Why it matters for your business: This is the #1 risk in business AI deployments. Always verify AI outputs against real data, especially for anything customer-facing or financial.
A technique where the AI searches your own documents, databases, or knowledge base before generating a response. Instead of relying on its training data alone, it retrieves relevant information and uses it to give grounded, accurate answers.
Why it matters for your business: RAG is how you make AI useful for your specific business — it can answer questions about your SOPs, customer data, or internal docs instead of giving generic responses.
Training an existing AI model on your specific data to make it better at a particular task. Like hiring a generalist and then training them on your industry — the base skills are there, but you’re sharpening them for your use case.
Why it matters for your business: Most businesses don’t need fine-tuning. RAG and good prompting handle 90% of use cases. Fine-tuning is for when you need consistently specialized outputs at scale.
The company behind ChatGPT and GPT models. Founded in 2015, they launched ChatGPT in November 2022 and triggered the current AI boom. Their models include GPT-4o (fast, good all-around), o1/o3 (slower, better at reasoning), and GPT-4.5 (their largest model).
Why it matters for your business: OpenAI has the largest market share and the most mature ecosystem of plugins and integrations. If you’re just getting started, ChatGPT is usually the first stop.
OpenAI’s consumer product — the chat interface most people use. ChatGPT is the app; GPT-4o is the model that powers it. The distinction matters because you can use GPT models through the API without ever touching ChatGPT.
OpenAI’s family of language models. “GPT” has become a generic term people use for any AI chatbot, but technically it only refers to OpenAI’s models: GPT-4o, GPT-4.5, o1, o3, etc.
The company behind Claude. Founded by former OpenAI researchers focused on AI safety. Their Claude models are known for strong writing quality, careful reasoning, and a large context window (200K tokens — the equivalent of a 400-page book in a single conversation).
Why it matters for your business: Claude often wins on long documents, nuanced writing, and following complex instructions. Many business owners find Claude produces higher-quality first drafts.
Anthropic’s AI assistant. Available as Claude.ai (the chat app) and through the API. Current models: Claude Opus (most capable), Claude Sonnet (balanced), and Claude Haiku (fast and cheap). Claude Projects let you upload documents and create persistent workspaces.
Why it matters for your business: Claude Projects are one of the most underused features in business AI. Upload your SOPs, brand guidelines, or client docs, and Claude becomes a specialist in your business.
Google’s AI models, branded as Gemini. Their advantage is deep integration with Google Workspace (Docs, Sheets, Gmail, Calendar). Gemini 2.0 and 2.5 Pro are competitive with GPT-4o and Claude Sonnet.
Why it matters for your business: If your business runs on Google Workspace, Gemini’s native integrations can save significant setup time compared to connecting other models via APIs.
Elon Musk’s AI company. Their model Grok is integrated into X (formerly Twitter) and has real-time access to posts on the platform. Grok 3 is competitive on benchmarks but has a smaller ecosystem than OpenAI or Anthropic.
Meta’s open-source AI models. “Open-source” means anyone can download and run them — no API fees, no data leaving your servers. Llama models are popular for businesses with strict data privacy requirements or high-volume use cases where API costs add up.
Why it matters for your business: You probably won’t use Llama directly, but it’s why AI pricing keeps dropping. Open-source competition forces the paid providers to lower prices and improve quality.
The way software talks to other software. When you build an AI agent, it uses an API to send prompts to the AI model and get responses back. APIs are how you go from “using ChatGPT in a browser” to “AI running inside your business systems.”
Why it matters for your business: The jump from chatbot to agent requires using APIs. Every cohort member who builds something production-grade learns to work with APIs.
Any system that does work without human intervention. AI automation specifically means using language models to handle tasks that previously required human judgment — reading emails, categorizing support tickets, drafting responses, extracting data from documents.
A sequence of steps that accomplish a business task. An AI workflow connects multiple steps — for example: receive purchase order PDF → extract line items → populate spreadsheet → notify the team.
Platforms that let you build software without writing traditional code. Tools like Zapier, Make, and n8n let you connect AI to your business systems using visual interfaces. “Low-code” means some coding but mostly drag-and-drop.
Why it matters for your business: Most business owners don’t need to learn Python. No-code tools can handle 80% of AI automations. The cohort teaches both approaches so you can pick what fits.
An open standard (created by Anthropic) that lets AI models connect to external tools and data sources in a standardized way. Think of it like USB for AI — a universal plug that works across different systems.
Why it matters for your business: MCP is becoming the standard for how AI agents interact with business tools. Understanding it helps you evaluate which tools will work together.
Multiple AI agents working together, each handling a different part of a task. One agent might research, another might write, and a third might review — like a small team where each member has a specialty.
Why it matters for your business: This is where the real power shows up. Rama Rao built a working prototype with four AI sub-agents in two days that would have taken six months with a human team.
The maximum amount of text an AI can process in a single conversation. Think of it as the AI’s working memory. Larger context windows mean you can give the AI more information at once — longer documents, more examples, more detailed instructions.
A setting that controls how creative or predictable AI responses are. Low temperature (0–0.3) = consistent, factual outputs. High temperature (0.7–1.0) = more creative, varied outputs. For business tasks like data extraction, use low. For brainstorming, use high.
A way to convert text into numbers so AI can measure how similar two pieces of content are. Used in search, recommendations, and RAG systems. When you search your company’s docs with AI, embeddings are what make it work.
Hidden instructions that set the AI’s behavior, personality, and constraints before a conversation starts. When you create a Claude Project or custom GPT, the system prompt is where you define who the AI is and how it should act.
Why it matters for your business: Well-crafted system prompts are the difference between a generic chatbot and a specialized business tool. This is one of the most important skills we teach.
An AI model whose code and weights are publicly available for anyone to download, modify, and run. Meta’s Llama, Mistral, and others. You can run them on your own servers, meaning no data leaves your control and no per-token API fees.
The process of an AI model generating a response. When you send a prompt and get an answer, that’s inference. Inference costs (measured in tokens) are what you pay for when using AI APIs.
The OwnerRx methodology: instead of asking “where can we use AI?”, start by identifying your biggest business constraint, then deploy AI against it. Adapted from Eli Goldratt’s Theory of Constraints.
Why it matters for your business: 70% of AI projects fail in their first year (BCG, 2025). Almost always because they start in the wrong place. Bottleneck-first ensures you’re solving the problem that actually matters.
A management philosophy developed by Eli Goldratt. Core idea: every system has one constraint that limits the whole system’s output. Improving anything that isn’t the constraint is waste. Find the constraint, break it, find the next one, repeat.
Why it matters for your business: This is the foundation of the OwnerRx approach. We don’t deploy AI everywhere — we deploy it at the constraint, which is where it produces the most value.
How prepared your business is to adopt AI effectively. Factors include: data quality, team willingness, process documentation, technology infrastructure, and leadership understanding of what AI can do.
The skill of writing effective instructions for AI. Includes techniques like few-shot prompting (giving examples), chain-of-thought (asking the AI to reason step by step), and role prompting (telling the AI to act as a specific expert).
Why it matters for your business: Better prompts = better results, for free. Before spending money on agents or automations, most businesses can get significant value just from better prompting.
Return on investment for AI projects. Calculate by measuring: hours saved per week × hourly cost of that labor, minus AI tool costs and implementation time. Most successful small business AI projects pay for themselves in 2–4 weeks.
Optimizing your website and content so AI search engines (ChatGPT, Perplexity, Claude) cite and recommend your business. Similar to SEO but for the AI era. Involves structured data, authoritative content, and specific formatting that AI models prefer.
Why it matters for your business: As more people use AI to search instead of Google, businesses that optimize for GEO will capture traffic that competitors miss entirely.
The cohort takes you from understanding these terms to building working AI agents for your business. 4 weeks, hands-on, practitioner-led.