This workshop will guide you through this development process. You'll start with the basic RAG stack, create an initial evaluation suite, and then experiment with different advanced techniques to improve RAG performance.
Large Language Models (LLMs) are revolutionizing how users can search for, interact with, and generate new content. Some recent stacks and toolkits around Retrieval-Augmented Generation (RAG) have emerged, enabling users to build applications such as chatbots using LLMs on their private data. However, while setting up a naive RAG stack is straightforward, having it meet a production quality bar is hard. To be an AI engineer, you need to learn principled development practices for evaluation and optimization of your RAG app - from data parameters to retrieval algorithms to fine-tuning.
This workshop will guide you through this development process. You'll start with the basic RAG stack, create an initial evaluation suite, and then experiment with different advanced techniques to improve RAG performance.
