Responsible AI is paramount in the realm of machine learning, especially with the growing call for AI regulation. This series addresses the practical needs of organizations and AI developers, offering hands-on learning to identify, debug, and mitigate issues associated with AI solutions. By leveraging both LLMs and traditional ML models, you’ll gain valuable insights to navigate the complexities of building AI solutions responsibly.

In this session, you’ll learn how to create visual executable flows that link LLMs, vector embeddings, prompts, and Python tools. You’ll also learn to evaluate performance metrics and responsible AI issues such as groundedness, hallucinations, relevance, etc.

Learn more about responsible AI principles and practices at aka.ms/Responsible-AIPrinciples

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