AI, in plain English
The words people throw around about AI, explained simply, with no jargon and no assumptions. If a term confuses you, that’s the term’s fault, not yours.
Start here
The core ideas everything else builds on.
- AI agent
A piece of software that uses AI to carry out a multi-step task on its own, like answering a customer, qualifying a lead, and booking a call, instead of just replying once.
The difference from a basic chatbot is that an agent can take actions (look something up, send a message, update a record) and make simple decisions along the way, within rules you set.
- AI system
AI built into how work actually gets done, so it runs the same way every time, instead of a one-off prompt you retype. A saved prompt, a connected tool, and a clear trigger turn a clever moment into a reliable system.
This is the difference between using AI and getting time back from it. A prompt is a moment; a system is a habit.
Related:AI agentAutomationAI wrapper
- Automation
A task that runs on its own when something happens, like a missed call triggering a text, or a finished job triggering a review request. AI makes automations smarter by handling the language and judgment parts.
- Chatbot
A program that holds a conversation with people. A basic one just answers questions; an AI agent goes further and can actually take action on your behalf.
- LLM (large language model)
The underlying technology behind tools like ChatGPT: a model trained on huge amounts of text that predicts and generates language, so it can draft, summarize, answer, and rewrite.
It's the engine, not the product. The value for a business comes from what you build around it: your context, your rules, and your workflow.
Related:AI agentRAG (retrieval-augmented generation)Hallucination
- Prompt
The instruction you give an AI. The quality of the prompt (who it's for, what good looks like, an example) usually decides whether the output is useful or generic.
Accuracy & trust
How AI stays correct, safe, and honest.
- Context window
How much text an AI can consider at once, measured in tokens. A bigger window means it can work with more of your document, conversation, or history at the same time.
Related:TokenLLM (large language model)
- Guardrails
The rules and limits you put around an AI so it stays safe and on-task: a fixed service area, no binding quotes, approved topics only, and a handoff to a human for anything outside the lines.
- Hallucination
When an AI states something confidently that is wrong or made up. It's a real risk, which is why anything that matters gets checked by a person, and why grounding (RAG) and guardrails exist.
Related:RAG (retrieval-augmented generation)GuardrailsHuman-in-the-loop
- Human-in-the-loop
A design where AI does the routine work but a person reviews or steps in for the parts that need judgment, accountability, or a real decision. The honest default for anything high-stakes.
Related:GuardrailsHallucination
- RAG (retrieval-augmented generation)
A way to ground an AI's answers in your own documents, so it responds from your real knowledge base instead of making things up. It looks up relevant source material first, then answers from it.
RAG is the main mechanism that keeps a support agent or assistant accurate and on-brand, and is how you reduce hallucination.
Getting found
Being seen and cited by search and AI.
- AEO (answer engine optimization)
Structuring your content so AI answer engines (ChatGPT, Perplexity, Google's AI overviews) can quote it directly: question-shaped headings, a clear answer up top, and structured data.
- GEO (generative engine optimization)
Making your business easy for AI tools to find, understand, and cite as a trusted source, the AI-era version of SEO. It leans on clear entity signals, structured data, and genuinely useful content.
Related:AEO (answer engine optimization)
Growth tactics
Specific ways AI wins and keeps customers.
- AI wrapper
A thin product that just passes your request to a model like ChatGPT and passes the answer back, with little real workflow, integration, or judgment around it. Wrappers are easy to copy and easy to churn out of.
The opposite is an embedded system that's wired into your tools and processes. The model is a commodity; the workflow is the moat.
- Missed-call text-back
An automation that instantly texts anyone whose call you miss, opening a conversation so they don't just call the next business. Useful anywhere calls get missed while you're busy or after hours.
Related:AutomationSpeed-to-lead
- Speed-to-lead
How fast you respond to a new lead. It matters more than almost anything else in sales: research on 15,000+ leads found contacting within 5 minutes makes a lead far more likely to qualify than waiting 30.
Related:AutomationMissed-call text-back
Technical terms
The acronyms developers use, decoded.
- API (application programming interface)
A way for two pieces of software to talk to each other. AI tools connect to your CRM, calendar, and website through APIs, that's how an agent can actually do things, not just chat.
Related:SDK (software development kit)MCP (model context protocol)Automation
- CLI (command-line interface)
A text-based way to run software by typing commands instead of clicking. Some AI tools run in the CLI, which is fast and scriptable once you're comfortable with it.
- Fine-tuning
Further-training a model on your own examples so it's better at one specific task. Often unnecessary, good prompting or RAG usually gets you there faster and cheaper.
Related:RAG (retrieval-augmented generation)LLM (large language model)
- IDE (integrated development environment)
The application developers write code in, like VS Code. Most now have AI built in to suggest, explain, and write code alongside the developer.
Related:CLI (command-line interface)SDK (software development kit)
- MCP (model context protocol)
An open standard that lets an AI assistant securely connect to your tools and data, so an agent can take real actions in your systems (read a file, update a record) instead of only talking.
Think of it as a universal adapter between AI and the apps you already use, which is why it's becoming a backbone for serious AI agents.
- Open-source model
A model whose weights are published so you can run or host it yourself, useful for privacy and control, versus a closed model you only reach through someone else's API.
Related:LLM (large language model)API (application programming interface)
- SDK (software development kit)
A bundle of ready-made code and tools that makes it faster for developers to build on a platform, like an AI provider's SDK for adding its model to an app.
Related:API (application programming interface)IDE (integrated development environment)
- Token
The unit AI reads and is billed in, roughly a piece of a word. Costs and limits are measured in tokens, and the context window is just how many tokens it can hold at once.
Want a term added, or one of these explained for your business? Ask us.