AI guardrails are the safeguards, rules, and technical controls placed around an AI system’s inputs and outputs to keep it operating safely, accurately, and within defined ethical and legal limits. In plain terms: guardrails are what stop an AI system from doing the wrong thing even when a user asks it to, or when it’s tempted
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Model drift is the gradual decline in a machine learning model’s predictive accuracy that happens when the statistical relationship between its inputs and the real world changes after the model was trained. In plain terms: your model was trained on yesterday’s world, and today’s world has quietly moved on without it. Model drift is one of
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AI observability is the practice of collecting and analyzing telemetry — logs, metrics, traces, and quality evaluations — from an AI system so that teams can tell not just whether it is running, but whether its outputs are correct, grounded, safe, and cost-efficient. In plain terms: can you tell why your AI gave a specific answer,
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