The ideas you need to understand before you build anything.
These aren’t product guides. They’re the underlying concepts that determine whether your deployment works or quietly dies.
The Ten Concepts
RAG
Retrieval-Augmented Generation. The pattern that makes AI agents actually useful — and the pattern that kills them when the retrieval layer rots.
AI Agent
What an AI agent actually is, what it isn’t, and why the term is so overloaded that it’s almost useless.
Shadow AI
Your employees are already using AI. You don’t know about it. You can’t control it. Here’s how to govern what you can’t see.
MCP
Model Context Protocol. The standard that makes tool connections explicit instead of fragile.
Data Residency
Where your data lives, where it gets processed, and why that matters for compliance, cost, and latency.
LLM Drift
The model that worked perfectly in January is wrong by June. Not because you changed anything. Because the provider did.
Self-Hosted AI
When to run models on your own hardware, when to use the cloud, and why the cost math isn’t what you think.
TCO
Total Cost of Ownership. The real numbers behind AI deployments — not the sticker price, the operating cost.
Vector Databases
The storage layer that powers retrieval. Which one to use, when to skip it, and why most teams overthink this.
Human-in-the-Loop
When to keep a human in the process, when to remove them, and why removing them too early is the most common mistake.
Each concept includes: what it is, why it matters, how it breaks, and the practical implications for a solo implementer.