The fastest way to ship a bad AI system is to start grading outputs before you've defined what a good one looks like. "Good" is a specification, not a vibe — and writing it down is the work most teams skip.
 

Before I evaluate a single answer, I define what correct, complete, and appropriately-toned look like for this use case and this audience. That definition becomes the rubric everything else is measured against. It turns "the AI feels off" into "the AI is failing criterion three, here's why."

"AI can make mistakes" is not a quality strategy. A confident wrong answer is worse than no answer — and the fix isn't a warning label, it's engineering.
 

I treat factuality and anti-hallucination as first-class design concerns: the knowledge has to be structured, sourced, and bounded so the model has less room to invent. You control the inputs, the retrieval, and the evaluation, and you measure hallucination the way you'd measure any other defect.

Most "AI problems" are knowledge problems wearing a model's clothes. Swap in a better model and a messy knowledge base still produces messy answers — now with more confidence.

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The durable work is underneath: information architecture, governance, and the discipline to keep knowledge current. Build that, and almost any capable model performs well on top of it. Skip it, and no model saves you. That's the whole thesis — AI is only as good as the knowledge underneath it.