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As generative AI shifts from prototypes to production, the real challenge isn’t just model quality, it’s building systems that teams can trust at scale. Developers and data engineers are being asked to deliver copilots, agents, and assistants that not only generate answers, but do so reliably, securely, and in alignment with business logic. In this luminary talk, Snowflake’s Chief Data & Analytics Officer, Anahita Tafvizi, distills lessons from Snowflake’s own deployments and conversations with leading builders. She unpacks the technical architecture behind trustworthy GenAI, including: How semantic layers prevent hallucination and preserve consistency Why observability and evaluation frameworks are non-negotiable for production AI Enforcing permissions, lineage, and testability across open and proprietary models Common developer patterns and anti-patterns seen across hundreds of enterprise use cases Whether you’re scaling an agent into production or just validating a pilot, this session offers a forward-looking blueprint for creating GenAI systems that are not just clever, but also credible, secure, and reusable setting the foundation for AI that endures. ❄Join our YouTube community❄ https://bit.ly/3lzfeeB Learn more about Snowflake: ➡️ Website: https://www.snowflake.com ➡️ Careers: http://careers.snowflake.com ➡️ Podcast page: https://www.snowflake.com/thedataclou... ➡️ Twitter: / snowflake ➡️ Instagram: / _snowflake_inc ➡️ Facebook: / snowflakedb ➡️ LinkedIn: https://bit.ly/2QUexl4 ➡️ Sign up for our weekly live demo program and have your questions answered by a Snowflake expert at https://bit.ly/2TdVCmJ Listen on: 🔈 Apple Podcasts: https://apple.co/3cCdrCU 🔈 Spotify: https://spoti.fi/39vCNjH 🔈 Simplecast: https://bit.ly/3rFCrgA #Snowflake #AIDataCloud #AI #GenAI #ArtificialIntelligence #agenticai