Toward Optimal Search and Retrieval for RAG
This work provides incremental insights for practitioners optimizing RAG systems for QA, addressing a known bottleneck in retrieval.
The study investigated how retrieval accuracy affects RAG pipeline performance in QA tasks, finding that lowering search accuracy has minor impact on RAG while potentially improving speed and memory efficiency.
Retrieval-augmented generation (RAG) is a promising method for addressing some of the memory-related challenges associated with Large Language Models (LLMs). Two separate systems form the RAG pipeline, the retriever and the reader, and the impact of each on downstream task performance is not well-understood. Here, we work towards the goal of understanding how retrievers can be optimized for RAG pipelines for common tasks such as Question Answering (QA). We conduct experiments focused on the relationship between retrieval and RAG performance on QA and attributed QA and unveil a number of insights useful to practitioners developing high-performance RAG pipelines. For example, lowering search accuracy has minor implications for RAG performance while potentially increasing retrieval speed and memory efficiency.