Reference
Plain-language definitions of the AI terms that actually matter — no jargon, no computer-science degree required.
An AI agent is a system that takes a goal, breaks it into steps, and executes those steps while making decisions along the way. It combines a language model (the reasoning brain), tools (actions it can take, like sending email or updating a spreadsheet), and instructions (plain-language rules for its behavior). Unlike a chatbot that only replies, an agent can act.
Vibe coding is building software by describing what you want in plain English and letting an AI model write the code. You direct and test; the AI implements. Coined by Andrej Karpathy in 2025, it lets non-developers build real, full-stack applications without writing syntax by hand, using tools like Cursor, Claude Code, Bolt, and Lovable.
Prompt engineering is the skill of communicating with AI models so they produce specific, high-quality, useful outputs. Because a language model's output quality is largely determined by input quality, a structured prompt — using a framework like CRAFT (Context, Role, Action, Format, Tone) — produces dramatically better results than a vague question.
A large language model is an AI system trained on vast amounts of text to understand and generate human-like language. LLMs such as ChatGPT (GPT), Claude, and Gemini are the reasoning engines behind most AI tools — they interpret instructions, make decisions, and generate text, code, and analysis.
No-code refers to tools that let you build software and automations through visual interfaces instead of writing code. No-code AI platforms like n8n, Make, and Zapier let non-technical people connect apps, trigger actions, and integrate AI into business workflows by dragging and configuring blocks rather than programming.
Workflow automation is using software to run repetitive, rule-based tasks automatically instead of doing them by hand. In an AI context, it connects triggers (like a form submission) to actions (like qualifying a lead with an AI model and sending an email), reclaiming hours of manual work. Common tools are n8n, Make, and Zapier.
Retrieval-augmented generation (RAG) is a technique that gives an AI model access to your own documents and data so it can answer questions using specific, up-to-date information instead of only its training data. It works by retrieving relevant content from a knowledge base and feeding it to the model, which powers accurate document Q&A bots and support agents.
Fine-tuning is the process of further training an existing AI model on your own examples so it adapts to a specific style, format, or task. It differs from prompting (giving instructions at runtime) and from RAG (retrieving external data): fine-tuning changes the model's behavior itself. For most business use cases, prompting and RAG are enough.
A token is the unit of text an AI model reads and generates — roughly three-quarters of a word in English. Language models process and price usage in tokens, and each model has a context window measured in tokens that limits how much text it can consider at once.
A context window is the maximum amount of text — measured in tokens — an AI model can consider at one time, including your prompt and its response. A larger context window lets the model work with longer documents, more conversation history, or bigger codebases without losing track of earlier information.
A hallucination is when an AI model generates information that sounds confident and plausible but is factually wrong or made up. Hallucinations happen because models predict likely text rather than look up facts. Techniques like retrieval-augmented generation (RAG), citations, and verification reduce them.
Multimodal AI is an AI system that can understand and generate more than one type of content — such as text, images, audio, and video — rather than text alone. Multimodal models can, for example, describe a photo, read a chart, or turn a sketch into working code.
An AI consultant helps businesses identify tasks that can be automated or enhanced with AI, then implements the solutions. The role has three parts: diagnosis (finding workflows worth automating), implementation (building them with no-code and AI tools), and education (teaching the team to maintain them). It requires no coding degree — just workflow, AI, and business-communication skills.
A micro-SaaS is a small, specialized software product that solves one specific problem for one specific audience, usually built and run by an individual or tiny team. In the AI era, non-technical founders build micro-SaaS products with vibe coding and AI features, charging a monthly subscription for a focused tool.
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