What terms does the glossary cover?

The prompt asks for core AI concepts every professional now meets — hallucination, prompt, token, context window, RAG, fine-tuning, agent, embedding, model, and more — and asks the model to add any terms especially relevant to your industry.

Why tailor it to an industry?

A generic definition of "RAG" means little to a lawyer or a nurse. Tailoring asks the model to ground each term in an example from your field, which makes the glossary far more useful for onboarding and training.

What does the audience level change?

Beginner produces jargon-free, analogy-led definitions for non-technical staff. Technical produces precise definitions that assume familiarity with software concepts. Choose based on who will read it.

Can I use this to train my team?

Yes. The output is designed as a shareable reference — paste it into a wiki, slide deck, or onboarding doc. Ask the model for a follow-up quiz to check comprehension.

Does the tool generate the glossary itself?

No. It builds the prompt in your browser; you run it in your own LLM. That keeps the tool free and lets you pick whichever model you trust.

AI in Your Industry Glossary Builder

An AI glossary builder solves a now-universal problem: AI vocabulary has spread into every industry faster than the explanations have. Staff hear “hallucination”, “RAG”, and “agent” in meetings without a shared, plain-English definition. This tool builds a prompt that makes an LLM generate a glossary tailored to your field, at the right reading level, with examples your team will recognise.

How it works

You choose your industry, an audience level (beginner or technical), and a term count. The builder assembles a prompt that lists the core AI terms everyone now encounters and instructs the model to define each one in plain English, illustrate it with an example drawn from your industry, and add any extra terms that matter specifically in your field. Beginner mode favours analogies and bans unexplained jargon; technical mode keeps definitions precise. The prompt is built entirely in your browser — you run it in whatever LLM you prefer, then paste the result into a wiki, deck, or onboarding doc.

Tips and examples

For onboarding non-technical teams, choose beginner and a modest term count (10–15) so the glossary is digestible — you can always generate a second, advanced pass later. Ask the model to follow up with a short quiz to confirm the definitions landed. Industry grounding is what makes this valuable: a “fine-tuning” example for a retailer (“adapting a model to your product catalogue”) lands far better than an abstract one. Keep the glossary in version control or a shared doc and regenerate it as the field moves — terms like “agent” and “MCP” shift meaning quickly, so an annual refresh keeps your team speaking the same language.