Most people using AI daily can't explain 12 basic terms.
Most people using AI daily can't explain 12 basic terms.
Not obscure ones, the ones that show up in every product announcement, every board deck, every job description.
I picked 12 that matter right now.
Not 12 for ML engineers.
12 for anyone who builds, buys, or decides anything involving AI in 2026.
Grouped them into four buckets:
→ The Foundations: LLM, Token, Context Window
The building blocks, what AI is, how it reads, and how much it can hold in its head.
→ How AI Learns: Fine-tuning, RAG, Hallucination
How models get smarter, stay grounded, and where they still fail.
→ How AI Works Today: Agentic AI, MCP, Multimodal
AI that acts, connects to your tools, and sees more than text.
→ Using AI Well: Prompt Engineering, Guardrails, Coding Agent
The skills and safety nets that separate experiments from production.
The one people underestimate: Guardrails.
Everyone wants to deploy AI.
Nobody wants to talk about what happens when it goes wrong.
Guardrails are the difference between a demo and a production system.
Are you already having that conversation with your team?
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♻️ Repost if this helped.