Every ontology article leaves you more confused than when you started. Here is the opposite - one real ontology running inside a banking AI, every piece of jargon translated (taxonomy included), the drift it killed, and what it becomes at enterprise scale.
Self-hosting an open model is not what keeps your data safe in a regulated industry - owning the front door is. I built an AI governance gateway on GCP with PII screening, token budgets, and a full audit trail for under 10 dollars a month, and it changed my answer on the whole build-vs-buy question.
I kept three copies of what my analyst agent was allowed to touch, and they drifted. Here's how the Open Knowledge Format cut it to one git-versioned source, compiled once and guarded by CI.
Every tool you know is depreciating. The one skill that isn't is your ability to learn unfamiliar things fast - and it quietly atrophies if you only ever work inside your comfort zone. Here's the case for deliberate brain breaks.
Ten quick questions on building retrieval-augmented systems that scale under load and don't leak data they shouldn't — access control, prompt injection, PII, evals, and caching. No reading required, just test yourself.
My chat bot confidently lied about my own writing. The bug wasn't retrieval - it was an ambiguous sentence in the source and a stale index. Why RAG is a data problem before it's a retrieval problem.
Ten questions on Beam's windowing methods, watermarks, triggers, and late data — with sourced explanations for every answer.
A short, high-level visual tour of the DAMA wheel: governance at the hub, the eleven knowledge areas around it, where data ethics fit, and my own attempt at slotting in AI.
