Postgres for Production Agents: Your Relational Foundation for Enterprise AI

Retrieval Augmented Generation (RAG) is now a fundamental pillar of enterprise AI, moving beyond initial adoption to production-grade applications. While the spotlight often shines on specialized vector databases, this session will present a compelling, practical argument for Postgres as the robust, scalable, and often superior foundation for production-grade context engineering.

As experienced engineers, we understand the value of battle-tested infrastructure. This talk will demonstrate how Postgres, enhanced by extensions like pgvector, can efficiently handle intelligent search and manage the rich, interconnected data, like user history and contextual information, essential for sophisticated AI applications. We'll dive deep into the practical advantages of a relational and ACID approach: transactional guarantees, data integration capabilities, simplified operational tooling, and robust data governance.

You'll walk away with practical tips for building and fine-tuning a complete RAG system on Postgres! We'll cover key implementation aspects, including data modeling strategies, performance tuning for large datasets, ensuring data synchronization, and navigating the nuances of integrating RAG within existing enterprise data ecosystems. Join this session to discover how to streamline your AI architecture, reduce operational overhead, and ground AI in your business facts with PostgreSQL.