Overview:

Retrieval-Augmented Generation (RAG), in all its diverse forms, is quickly becoming the backbone of enterprise AI systems, enabling organizations to combine the reasoning power of large language models with the context of their own data. But moving from proof-of-concept to production takes more than just plugging in a vector database. It requires thoughtful planning around security, data governance, and performance evaluation.

In this webinar, we explored what it took to implement RAG at enterprise scale—covering the technical foundations, organizational alignment, and practical methods for evaluating and improving system quality over time. We also discussed how enterprises were measuring success by focusing on end-user experience and business outcomes, ensuring that AI solutions didn’t just work, but delivered real value.

Technical Details:

In this session, we walked through:

• How to secure enterprise RAG deployments and manage sensitive data access
• Strategies for continuous evaluation (Evals) to maintain quality and reduce hallucinations
• How RAG systems were helping employees tackle complex, high-value business problems
• How RAG was being leveraged by Agentic Systems