Noelle RussellChief Executive Officer, AI Leadership Institute · 5x Microsoft Trustworthy AI MVP | 3.4+ million students | #1 Thought Leader in Agentic AI | AI-Powered Revenue Growth + Cost Optimization Expert ✨Best-Selling Author: Scaling Responsible AI | TEDx Sp

Azure Content Safety for Responsible AI at Scale

15 Aug 2024 · 1 min

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The video emphasizes the importance of maintaining quality and reliability in generative AI outputs, highlighting that large language model (LLM) hallucinations undermine trust by producing ungrounded or fabricated information. It explains that a grounded response stays factual, while hallucinated content can spread misinformation. The speaker advocates for using groundedness detection to identify and mitigate ungrounded content, thereby enhancing the trustworthiness of foundation model outputs. The presentation concludes by promoting Azure AI Content Seeker as a tool to help developers build generative AI responsibly and encourages viewers to get started with it.

Chapters

In the world of generative AI, supporting the quality and reliability of model outputs is crucial. But large language model hallucinations threaten this reliability and quality. A grounded response adheres to the facts, avoiding speculation or fabrication, while an ungrounded or hallucinated response can lead to inaccuracies and misinformation. Enhance the quality and trustworthiness of your foundation model outputs by pinpointing ungrounded or hallucinated content with groundedness detection. Build generative AI responsibly. Get started with Azure AI Content Seeker.