I was practicing for a Deep Research course — inside the course AI itself.
My answer to a practice question was three compressed concepts:
Flipped Interaction. RAG. Cross-domain.
The AI responded confidently and completely wrong.
RAG, it explained, stands for Red, Amber, Green — a project management status indicator useful for identifying areas that need attention.
It wasn’t slightly off. It was in a completely different field.
And it was entirely my fault.
I’ve been working with Retrieval Augmented Generation for months. Writing about it. Teaching it. It’s become single-word shorthand in my thinking. I forgot that compressed expert language and AI disambiguation are a dangerous combination.
The AI had no context. So it picked the most statistically common meaning and proceeded with complete confidence.
No flag. No clarifying question. Just a smooth, authoritative, wrong answer.
The governance implication is significant.
The more expert you are, the more vulnerable you are to this specific failure. Experts compress. AI expands in the wrong direction. And the result is confidently incorrect output that only someone with domain knowledge would catch.
A junior employee using the same tool would have accepted that answer without question.
The architectural solution is context delivery.
Model Context Protocol and Agent Communication Protocol exist precisely for this. Pre-load the system with who you are, what your vocabulary means, what domain you operate in. The disambiguation happens at the protocol layer — before the conversation starts — not inside the prompt where it’s easy to forget.
One protocol layer eliminates the entire failure mode.
The lesson I’m taking away:
Fluency is a trap. Always deliver context. And never assume the AI is in the same room you are.
Rob van Linda — Transformation Consultant, AI Governance Specialist, and apparently still capable of making rookie mistakes after months of daily AI collaboration.
“Human Before the Loop” — because sometimes the human is the problem too.
