The AI Dictionary — Every Term You Need to Know
AI, ML, LLM, RAG, MoE, frontier models, sovereignty. If you have ever nodded along in a meeting where someone used five of those words interchangeably, the capstone glossary that decodes the field — organized in the order you actually need to learn it.
The Promise
- A shared vocabulary across the executive team — when 'model,' 'AI,' and 'LLM' stop meaning whatever the loudest person thinks they mean, governance conversations become possible.
- Pattern recognition for sales pitches — once you know what RAG, MoE, and 'agentic' actually denote, you can hear the gap between a vendor's claim and what their architecture can deliver.
- A reference you come back to — the terms cluster, so learning them in the right order builds a mental map instead of a flashcard pile.
- A bridge to Season 2 — every applied AI conversation in the next season assumes this vocabulary; locking it in now is what makes the rest land.
The Risk
- Vocabulary fluency mistaken for technical literacy — knowing what 'transformer' means is not the same as knowing where a transformer-based system will fail.
- Acronym creep from vendors — new labels appear faster than the underlying ideas change, and the dictionary needs updating roughly every twelve months to stay useful.
- Definition wars — two teams using the same term to mean different things is the most common AI governance failure I see, and a dictionary only helps when both sides agreed to use it.
- False precision — clean definitions make a messy field look settled. The terms in this episode have boundaries that get fuzzy at the edges, and pretending otherwise is its own risk.
What you’ll learn
The capstone of Season 1: a structured walkthrough of the AI vocabulary every business leader needs. Six clusters of terms — from the foundational stack (AI, ML, deep learning, neural networks) up through the architectures shaping today’s frontier (LLMs, MoE, RAG, agents) and the geopolitics that decides who deploys what (sovereignty, open weights, frontier labs). Each term gets cross-referenced back to the earlier episodes where it first appeared, so the language locks in rather than floating loose.
Why a dictionary is worth twenty minutes
The most expensive AI mistakes I see in enterprises do not start with bad technology. They start with two people in a meeting using the same word to mean different things — and a decision gets made before anyone notices. A board member hears “AI” and pictures a chatbot. The CTO hears “AI” and pictures a fine-tuned LLM with RAG over the data warehouse. The procurement team hears “AI” and pictures a SaaS vendor’s marketing page. All three approve the budget, and the project drifts for a year before anyone agrees on what it was supposed to do.
A shared vocabulary fixes that — not by making everyone technical, but by making the gaps visible. When the dictionary is in place, the questions get sharper. “Is this RAG or fine-tuning?” “Is the model open-weight or API-only?” “Does ‘agentic’ here mean autonomous, or scripted-with-tools?” Those are the questions that catch a bad procurement decision before it becomes a bad procurement decision.
What comes next
Season 2 takes this vocabulary and applies it — to specific workflows, specific vendor categories, and specific governance problems. Without the words, those conversations are impossible. With them, they’re the only ones worth having.