In this session we will dive into the practical realities of deploying a Retrieval-Augmented Generation (RAG) application. This lecture is designed to equip you with invaluable insights and strategies derived from real-world implementation experiences.

We'll dive into the practical challenges that arise when moving from concept to production, sharing tried-and-true improvements and innovative solutions that address these hurdles.

We will then discuss some potential ways to incorporate visual elements like images and charts, manage the complexities of ingesting large data files, and share some cost optimization techniques that ensure efficiency with limited impact on performance, and we’ll share some of our learnings of building a scalable ingestion pipeline.

We'll also explore the current limitations of RAG architecture, providing a realistic perspective on its constraints and areas for growth.

Finally, we'll venture into advanced concepts that push the boundaries of what's possible with RAG, such as the potential of autonomous agents, fine-tuning for optimal model performance, and the advancements in GraphRAG.