"One page, the whole vocabulary. Once these terms feel familiar, AI articles, docs, and job posts read like plain English."
Level: Reference · Time: ~browse · Prerequisites: none
Learning Objectives
By the end of this module, you will be able to:
- Recognize the key terms from across the path
- Explain each in simple language
- Use this page as a quick reference
This page gathers the vocabulary from the whole path into one place. Don't try to memorize it in a sitting — that's not how technical vocabulary sticks. Instead, skim it now to see the shape of the field, then return whenever a term feels hazy while you build. Terms cement through use: the third time you meet "embedding" in a real task, it stops being a definition and becomes an instinct. The four groups below mirror the course's four big movements, so a term's neighbors hint at when it matters.
Explain like I'm new: Learning a field is partly learning its language. Once these words feel ordinary, a dense AI blog post or job description stops looking like a wall of jargon and starts reading like a normal sentence — which is exactly when you know you belong in the conversation.
Foundations & ML
| Term | Plain meaning |
|---|---|
| Artificial Intelligence | Machines doing tasks that need "intelligence" |
| Machine learning | Learning patterns from data instead of hand-written rules |
| Deep learning | ML using many-layered neural networks |
| Neural network | Layers of simple units ("neurons") with learnable weights |
| Weights | The numbers a network adjusts as it learns |
| Training / inference | Learning from data / using the trained model |
| Features / label | Inputs (X) / the answer to predict (y) |
| Overfitting | Memorizing noise; fails on new data |
| CNN | Network great at images |
| Transformer | Architecture using "attention"; powers modern AI |
| Attention | Weighing how much each word relates to others |
Generative AI & LLMs
| Term | Plain meaning |
|---|---|
| Generative AI | AI that creates new content |
| LLM | Large Language Model — predicts the next token |
| Token | A chunk of text (~¾ word) |
| Context window | How much text a model considers at once |
| Prompt | The instruction you give a model |
| Hallucination | Confident but false model output |
| Embedding | Text turned into meaning-capturing numbers |
| RAG | Retrieve facts, then answer from them |
LangChain & MCP
| Term | Plain meaning |
|---|---|
| LangChain | Framework of building blocks for LLM apps |
| Chain | Connected steps (prompt → model → parser) |
| Agent | LLM that decides its own steps using tools |
| Tool / function calling | An action the model can request; app runs it |
| Vector store | Database of embeddings for semantic search |
| MCP | Model Context Protocol — "USB-C for AI" |
| Host / client / server | The MCP roles (app / connector / capability provider) |
| Resources / prompts (MCP) | Read-only data / reusable templates a server offers |
| Prompt injection | Hidden malicious instructions in read content |
Try this: Pick any three terms above you couldn't have defined a month ago and say each aloud in one plain sentence, without peeking. The ones that come easily are truly yours; the ones that stall are your short revision list. That five-minute self-check is more useful than re-reading the whole table.
Edge AI & Production
| Term | Plain meaning |
|---|---|
| Edge AI | Running models on local devices |
| SLM | Small Language Model — runs on-device |
| Quantization | Fewer bits per weight — smaller, faster |
| Foundry Local / runtime | Software that runs models locally |
| MLOps / SLMOps | Operating models in production / on the edge |
| Pipeline | Automated ML steps (ingest → … → deploy) |
| Serving | Exposing a model as a live API |
| Feature store | Consistent features for training + serving |
| Drift | Accuracy slipping as the world changes |
| CI/CD/CT | Automated test/deploy, plus continuous training |
| Container / Kubernetes | Packaged app / orchestrator for scaling services |
Key idea: See the arc: foundations explain how AI learns; GenAI/LLMs are today's models; LangChain/MCP connect them to prompts, data, and tools; Edge AI runs them on devices; production ships and sustains them. That's the whole "Zero to AI Engineer" path in one map.
Notice how the terms depend on each other, which is why the arc matters more than any single row. You can't really grasp RAG without embedding and vector store; drift only makes sense once you understand training versus inference; quantization presupposes you know what weights are. A glossary looks like a flat list, but the concepts form a small web — and seeing that web is what turns scattered definitions into a working mental model of how a real AI system fits together.
Key Takeaway: These ~40 terms span the entire path — foundations and ML, generative AI and LLMs, LangChain and MCP, edge AI, and production engineering. Bookmark this page; it's your quick reference as you build.
Further Learning
Part of "Zero to AI Engineer." Terms adapted from Microsoft's open curricula (MIT License).