The document discusses the role of Quality Engineering (QE) in the successful adoption of generative AI. It emphasizes the need for enterprises to integrate quality processes into generative AI workflows to mitigate risks like data leakage, bias, and regulatory issues. The quality function ensures safe AI use through governance, model evaluation, and compliance. By leveraging AI to enhance testing and automating quality assurance, enterprises can accelerate the deployment of generative AI while minimizing risks. The guide also highlights the new roles within QE, including data scientists and AI strategists, and the evolving importance of sustainability in AI adoption.

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