Building Performant RAG Applications for Production

GOTO Copenhagen 2024

In today's rapidly evolving technological landscape, Large Language Models (LLMs) are transforming AI applications but often lack specific knowledge outside their training data. Enter Retrieval Augmented Generation (RAG), offering a compelling solution to bridge these knowledge gaps. Transitioning baseline RAG applications to production, however, present challenges that might prevent applications from exiting the prototyping stage.

Our presentation will explore how to develop production-ready RAG applications, highlighting the common challenges and advanced techniques needed to overcome them. Attendees will gain insights into ensuring flexibility, reliability, predictability, and scalability in their RAG pipelines, enabling them to handle diverse and complex tasks. Supplemented by a realistic use case and practical code examples, we will equip developers with a robust toolkit for building high-performance RAG applications. We will delve into the nuances of RAG, demonstrating its transformative potential and providing you with the knowledge to harness its full capabilities in your own applications