A feedback loop captures real-world user interactions and quality ratings. Those signals can be analyzed to refine prompts or fine-tune the foundation model, leading to continuous accuracy and relevance improvements. Scaling hardware resources, forcing deterministic outputs, or encrypting model weights are valuable practices, but they are not the goal of a feedback loop in the foundation model lifecycle.
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AWS Certified AI Practitioner AIF-C01
Fundamentals of Generative AI
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