# Toward Optimal Search and Retrieval for RAG

arXiv: https://arxiv.org/abs/2411.07396 · HTML: https://mlsanity.com/paper/2411.07396/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation.
- Builds on 5 of its 35 references that are in the map.
- First posted on arXiv 11 Nov 2024.

## Placed in

- Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/)

## Builds on (most cited first, 5 of 5)

- [MTEB: Massive Text Embedding Benchmark](https://mlsanity.com/paper/2210.07316/) (2022)
- [Enabling Large Language Models to Generate Text with Citations](https://mlsanity.com/paper/2305.14627/) (2023)
- [Making Retrieval-Augmented Language Models Robust to Irrelevant Context](https://mlsanity.com/paper/2310.01558/) (2023)
- [Evaluating Retrieval Quality in Retrieval-Augmented Generation](https://mlsanity.com/paper/2404.13781/) (2024)
- [RAGGED: Towards Informed Design of Scalable and Stable RAG Systems](https://mlsanity.com/paper/2403.09040/) (2024)

## Built on by (most cited first, 4 of 4)

- [Evaluating List Construction and Temporal Understanding capabilities of Large Language Models](https://mlsanity.com/paper/2506.21783/) (2025)
- [Multi-Value-Product Retrieval-Augmented Generation for Industrial Product Attribute Value Identification](https://mlsanity.com/paper/2509.23874/) (2025)
- [Automatically Inferring Teachers' Geometric Content Knowledge: A Skills Based Approach](https://mlsanity.com/paper/2604.13666/) (2026)
- [A Systematic Evaluation of Retrieval-Augmented Generation and Language Models for Space Operations](https://mlsanity.com/paper/2605.27444/) (2026)

## Abstract (arXiv)

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.