Supercharging Your RAG System: Techniques and Challenges // Tengyu Ma // DE4AI

//Abstract
Retrieval-augmented generation is the predominant way to ingest proprietary unstructured data into generative AI systems. First, I will briefly state my view on the comparison between RAG and other competing paradigms such as finetuning and long-context LLMs. Then, I will briefly introduce embedding models and rerankers, two key components responsible for the retrieval quality. I will then discuss a list of techniques for improving the retrieval quality, such as query generation/decomposition and proper evaluation methods. Finally, I will discuss some current challenges in RAG and possible future directions.
//Bio
Co-founder & CEO of Voyage AI
Assistant Professor of Computer Science at Stanford University
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