AI Governance
AI governance is the system that determines who can deploy AI, under what conditions, with what oversight, turning ad hoc experimentation into accountable organizational practice.
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AI governance is the system that determines who can deploy AI, under what conditions, with what oversight, turning ad hoc experimentation into accountable organizational practice.
When a model is wrong, or right for the wrong reasons, and no one catches it, the decisions it drives keep compounding the error. That's model risk.
Hallucinations are AI outputs that are confidently stated but factually wrong. The model isn't lying or guessing, it's generating plausible-sounding language that happens to be false.
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Generative AI produces new content—text, images, code, summaries, audio—on demand, based on patterns learned from vast amounts of existing data.
Shadow AI is what happens when employees use AI tools the organization hasn't approved, usually because the approved options don't meet their needs.
Prompt engineering is the practice of writing clear instructions for an AI system, specifying the task, context, format, and constraints, so it produces more useful, consistent output.
RAG connects a generative AI model to your organization's documents so it answers from what you actually know, not just what the model was trained on.
AI governance is the system that determines who can deploy AI, under what conditions, with what oversight, turning ad hoc experimentation into accountable organizational practice.
Hallucinations are AI outputs that are confidently stated but factually wrong. The model isn't lying or guessing, it's generating plausible-sounding language that happens to be false.