You Do Not Need a Computer Science Degree. You Need a Translator.
Every week, we sit down with founders and business owners who are smart, successful people running real companies. And every week, at least one of them says some version of: "I feel like everyone is speaking a different language when they talk about AI."
They are right. The AI industry has done a spectacular job of making simple concepts sound impenetrable. Half the jargon exists to make vendors sound smarter than they are. The other half describes genuinely important concepts that you need to understand before you sign a contract or make a hiring decision.
This is the glossary we give every new client at OneWave. Plain English. Business context. No fluff. Bookmark it, share it with your team, and stop nodding along in meetings when someone drops a term you do not recognize.
Half the jargon exists to make vendors sound smarter than they are. The other half describes genuinely important concepts you need to understand before you sign a contract or make a hiring decision.
The Basics: What AI Actually Is
LLM (Large Language Model)
Plain English: A piece of software trained on massive amounts of text that can read, write, and reason about language.
Why it matters for your business: LLMs are the engine behind every AI chatbot, writing tool, and automation you have heard about. When someone says "we will use AI to automate your customer support," they almost certainly mean an LLM.
GPT
Plain English: A specific family of LLMs made by OpenAI. GPT stands for Generative Pre-trained Transformer. It is a brand name, not a generic term.
Why it matters for your business: People use "GPT" the way they use "Kleenex" -- as a generic term for all AI models. It is not. There are many LLMs from different companies, and GPT is just one family. When a vendor says "we use GPT," ask which version and why they chose it over alternatives.
Claude
Plain English: An LLM made by Anthropic. It is the model we use most at OneWave because it excels at business tasks like analysis, writing, and following complex instructions.
Why it matters for your business: Claude is generally better at nuanced business tasks, longer documents, and following detailed instructions. It is also built with a stronger emphasis on safety and accuracy. We wrote in detail about why we bet on Anthropic over OpenAI if you want the full reasoning. If your vendor is not at least considering Claude alongside GPT, they may not be evaluating options thoroughly.
Model
Plain English: The AI software itself. When someone says "which model are you using," they mean which specific AI system -- Claude Opus, GPT-4, Gemini, and so on.
Why it matters for your business: Different models have different strengths, costs, and speed. The model choice directly affects your results and your bill. Cheaper models are faster but less capable. Premium models cost more per query but handle complex tasks better. Your vendor should be able to explain why they chose a specific model for your use case.
How AI Thinks: The Concepts Behind the Curtain
Prompt
Plain English: The instruction you give to an AI. It is the text you type into the chat box, but in a business context, it also refers to carefully crafted instructions that tell the AI exactly how to behave.
Why it matters for your business: The quality of your prompt determines the quality of the output. A vague prompt gets a vague answer. A specific, well-structured prompt gets a precise, useful result. This is why "prompt engineering" is a real skill, not a buzzword.
Context Window
Plain English: The amount of text an AI can read and consider at one time. Think of it as the AI's working memory -- how much information it can hold in its head during a single conversation.
Why it matters for your business: If you need an AI to analyze a 200-page contract, the context window needs to be large enough to hold the whole thing. Context windows have grown dramatically -- Claude can now handle over a million tokens (roughly 700,000 words) in a single session. This is a major practical advantage for business use cases involving long documents. For a deeper dive on the distinction between context and memory, see our post on agent memory vs. context windows.
Tokens
Plain English: The units AI uses to measure text. Roughly, one token equals about three-quarters of a word. "Hello world" is two tokens. A typical business email is about 200-400 tokens.
Why it matters for your business: You are billed by tokens. Every word you send to the AI and every word it sends back costs money. Understanding tokens helps you estimate costs and optimize your AI workflows to avoid wasting money on unnecessary input.
Hallucination
Plain English: When an AI confidently states something that is factually wrong. It does not "know" it is wrong. It generates text that sounds plausible but is fabricated.
Why it matters for your business: This is the single biggest risk of using AI without proper oversight. An AI might cite a court case that does not exist, invent a statistic, or misrepresent a contract clause. Every AI-generated output in your business needs human verification, especially for anything client-facing, legal, or financial.
Temperature
Plain English: A setting that controls how creative or predictable the AI's responses are. Low temperature means more predictable, consistent outputs. High temperature means more creative, varied outputs.
Why it matters for your business: For business tasks like data extraction, contract review, or reporting, you want low temperature -- consistency matters more than creativity. For brainstorming, marketing copy, or creative tasks, higher temperature can produce better results. Your vendor should be tuning this for each workflow.
Training Data
Plain English: The massive collection of text that an AI model learned from during its creation. This includes books, websites, articles, and other publicly available text.
Why it matters for your business: The training data determines what the model "knows." It also has a cutoff date -- the model does not know about events after its training ended. This matters when you need current information or industry-specific knowledge that may not be well-represented in the training data.
Inference
Plain English: The process of an AI generating a response. When you ask Claude a question and it answers, that is inference. Training is how the AI learns. Inference is how the AI works.
Why it matters for your business: Inference costs money every time it runs. When you are evaluating AI costs, you are mostly paying for inference -- the compute power required every time the model processes a request and generates a response.
Connecting AI to Your Business: Integration Concepts
Fine-Tuning vs RAG vs Prompt Engineering
These three terms describe different ways to make an AI work better for your specific business. They are often confused, and picking the wrong approach wastes time and money.
- Prompt Engineering is writing better instructions. It is the cheapest, fastest approach and should always be your first step. Think of it as teaching someone by giving them a really detailed brief.
- RAG (Retrieval-Augmented Generation) is giving the AI access to your company's documents so it can look up relevant information before answering. Think of it as giving someone a reference library. The AI searches your documents, finds relevant sections, and uses them to generate a response.
- Fine-Tuning is actually modifying the AI model itself using your data. Think of it as sending someone to a specialized training program. It is expensive, time- consuming, and rarely necessary for most business use cases.
Why it matters for your business: If a vendor jumps straight to fine-tuning, be skeptical. Most business problems are solved with good prompt engineering and RAG. Fine- tuning is a last resort, not a first step. We tell clients: start with prompts, add RAG if you need company-specific knowledge, and only consider fine-tuning if the first two approaches genuinely fall short.
AI Agent vs Chatbot vs Assistant
These terms get used interchangeably, but they describe very different levels of capability.
- Chatbot: Responds to messages in a conversation. It can answer questions and generate text, but it cannot take actions or use tools. Think customer support chat widget.
- Assistant: A chatbot with access to some tools. It can search documents, look up information, or perform simple tasks within a defined scope. Think Siri or Alexa, but smarter.
- AI Agent: An AI system that can reason, plan, use multiple tools, and execute multi-step tasks independently. It can decide what to do next, recover from errors, and complete complex workflows with minimal human intervention. This is where the real business value lives.
Why it matters for your business: If someone is selling you a "chatbot" and calling it an "agent," you are overpaying. If someone is selling you an actual agent and you are treating it like a chatbot, you are leaving value on the table. Know what you are buying. For a full breakdown of the difference, read what is an AI agent and why your business needs one.
MCP (Model Context Protocol)
Plain English: A standard way to connect AI models to external tools and data sources. Think of it as a universal adapter -- like USB-C for AI. Instead of building custom integrations for every tool, MCP provides a single protocol that any AI can use to talk to any tool.
Why it matters for your business: MCP is quietly becoming the standard for AI integration. It means your AI workflows are not locked into one vendor's ecosystem. If your AI setup uses MCP, you can swap models, add new tools, and scale without rebuilding everything from scratch. We build all our client integrations on MCP for exactly this reason. We go much deeper on the topic in MCP servers explained.
Embedding and Vector Database
Plain English: An embedding is a way to convert text into numbers so that similar concepts end up with similar numbers. A vector database stores these number representations so you can quickly find documents that are related to a query. Together, they power the "search" part of RAG.
Why it matters for your business: If you want an AI that can search your company's knowledge base, you need embeddings and a vector database. This is the technology behind "AI-powered search" and "intelligent document retrieval." It is also what makes it possible for an AI to find the relevant section of a 500-page manual in milliseconds.
API (Application Programming Interface)
Plain English: A way for two pieces of software to talk to each other. When your app sends a question to Claude and gets an answer back, it is using Claude's API. Think of it as a menu at a restaurant -- the API defines what you can order and how to order it.
Why it matters for your business: APIs are how AI gets built into your existing tools. Without an API, your team has to copy-paste between a chat interface and your other software. With an API, the AI is embedded directly into your workflow -- it reads from your CRM, writes to your project management tool, and updates your database automatically.
Webhook
Plain English: An automatic notification from one system to another when something happens. If an API is you calling the restaurant, a webhook is the restaurant calling you when your table is ready.
Why it matters for your business: Webhooks are what make AI workflows reactive instead of manual. When a new lead fills out a form, a webhook can trigger an AI agent to qualify the lead, draft a response, and update your CRM -- all without anyone clicking a button.
Common Buzzwords Decoded
Parameters
Plain English: The internal settings of an AI model that were learned during training. When someone says a model has "100 billion parameters," they mean it has 100 billion learned values that determine how it processes and generates text. More parameters generally means more capable, but also more expensive to run.
Why it matters for your business: Parameter count is a rough proxy for model capability, but it is not the only factor. A well-designed smaller model can outperform a poorly designed larger one. Do not let a vendor impress you with parameter counts alone. Ask about benchmark performance on tasks relevant to your use case.
What to Do With All This
You do not need to memorize every term in this guide. What matters is having a reference you can check when a vendor drops jargon, when you are reading an AI proposal, or when your team is discussing implementation options.
Here is our practical advice: print this out (or bookmark it) and bring it to your next vendor meeting. If someone uses a term and cannot explain it in plain English when you ask, that is a red flag. The best AI practitioners can explain what they do in language that anyone can understand. The ones who hide behind jargon are usually hiding a lack of substance.
The AI landscape is moving fast, and new terms will emerge. But the fundamentals in this guide will stay relevant for years. Understanding what an LLM is, how context windows work, why RAG matters, and what an AI agent can actually do -- that knowledge puts you in a position to make informed decisions instead of expensive guesses.
The best AI practitioners can explain what they do in language that anyone can understand. The ones who hide behind jargon are usually hiding a lack of substance.