---
title: "Searching for Best Practices in Retrieval-Augmented Generation"
description: "Choosing among these options affects both answer quality and latency, yet the authors found no prior… 77 references, cited by 39 papers here."
authors: ["Xiaohua Wang", "Zhenghua Wang", "Xuan Gao", "Feiran Zhang", "Yixin Wu", "Zhibo Xu", "Tianyuan Shi", "Zhengyuan Wang", "Shizheng Li", "Qi Qian", "Ruicheng Yin", "Changze Lv", "Xiaoqing Zheng", "Xuanjing Huang"]
year: 2024
venue: "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/2024.emnlp-main.981/"
doi: "10.18653/v1/2024.emnlp-main.981"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wang-2024-searching-best-practices-retrieval-augmented/"
---
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# Searching for Best Practices in Retrieval-Augmented Generation

**Authors:** [Xiaohua Wang](https://md.research.elamur.ai/free/en/authors/xiaohua-wang/), [Zhenghua Wang](https://md.research.elamur.ai/free/en/authors/zhenghua-wang/), Xuan Gao, [Feiran Zhang](https://md.research.elamur.ai/free/en/authors/feiran-zhang/), [Yixin Wu](https://md.research.elamur.ai/free/en/authors/yixin-wu/), [Zhibo Xu](https://md.research.elamur.ai/free/en/authors/zhibo-xu/), [Tianyuan Shi](https://md.research.elamur.ai/free/en/authors/tianyuan-shi/), Zhengyuan Wang, [Shizheng Li](https://md.research.elamur.ai/free/en/authors/shizheng-li/), [Qi Qian](https://md.research.elamur.ai/free/en/authors/qi-qian/), Ruicheng Yin, [Changze Lv](https://md.research.elamur.ai/free/en/authors/changze-lv/), [Xiaoqing Zheng](https://md.research.elamur.ai/free/en/authors/xiaoqing-zheng/) and [Xuanjing Huang](https://md.research.elamur.ai/free/en/authors/xuanjing-huang/) · **Year:** 2024 · **Venue:** [Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/emnlp/) ([EMNLP 2024](https://md.research.elamur.ai/free/en/venues/emnlp/2024/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2024.emnlp-main.981/) · [PDF](https://aclanthology.org/2024.emnlp-main.981.pdf) · [DOI 10.18653/v1/2024.emnlp-main.981](https://doi.org/10.18653/v1/2024.emnlp-main.981) · [OpenAlex](https://openalex.org/W4404782883)

**Citations:** 111 citations (OpenAlex, 2026-10-14) · [39](https://md.research.elamur.ai/free/en/papers/wang-2024-searching-best-practices-retrieval-augmented/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Retrieval-augmented generation (RAG) pipelines consist of many stages, and each stage can be built in several ways. Choosing among these options affects both answer quality and latency, yet the authors found no prior systematic study of the full workflow. They split RAG into modules: query classification, chunking, embedding, vector database, retrieval, reranking, repacking, summarization and generator fine-tuning. They then search for good configurations in three steps. First, they shortlist strong candidates per module. Second, they swap one module's method at a time while the rest stay fixed, keeping each winner for later experiments. Third, they test promising combinations for different trade-offs. They also propose an evaluation framework that covers general ability, domain-specific tasks and retrieval-related skills. The study yields two recommended setups. One maximizes quality and reaches an average score of 0.483 at high compute cost. The other is cheaper and keeps comparable results. A multimodal extension shows that retrieving stored images can stand in for generating new ones. These findings give practitioners evidence-based defaults instead of ad hoc choices.

## Key points
- **Query classification:** the authors built a 111K-sample dataset covering 15 task types (64K need retrieval, 47K do not). A classifier trained on it decides when retrieval can be skipped, which saves time.
- **Highest-quality setup:** monoT5 for reranking, Reverse ordering for repacking and Recomp for summarization. It achieved an average score of 0.483 but is computationally heavy.
- **Balanced setup:** query classification, Hybrid retrieval, TILDEv2 reranking, Reverse repacking and Recomp. Retrieval dominates runtime, so the Hybrid choice cuts latency substantially.
- **Options compared:**
  - Retrieval: BM25, Contriever, LLM-Embedder, HyDE, query rewriting and decomposition.
  - Embedding models: e5, bge, Jina-embeddings-v2, Gte, all-mpnet-base-v2.
  - Vector databases: Milvus, Faiss, Weaviate, Qdrant, Chroma.
  - Summarizers: LongLLMlingua, SelectiveContext.
- **Multimodal "retrieval as generation":**
  - Setup: PartiPrompts prompts were used both to generate images with Stable Diffusion and to retrieve images from CC3M.
  - Scoring: CLIP similarity (openai/clip-vit-large-patch14) compared the two outputs.
  - Result: retrieval greatly reduced time while preserving image quality.
  - Code: https://github.com/FudanDNN-NLP/RAG.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Fudan University](https://md.research.elamur.ai/free/en/universities/fudan-university/) — University, Shanghai, China · 167 works · 20,987 citations

📚 Shelves (5): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 15,891 works · [🗄️ Information Systems](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · 1,437 works

🧭 Topics (1): [🔍 Information retrieval & search](https://md.research.elamur.ai/free/en/topics/information-retrieval/) · 473 works

💡 Concepts (1): [💡 Retrieval-augmented generation](https://md.research.elamur.ai/free/en/concepts/retrieval-augmented-generation/) · 50 works

## Cite

Cite as: Xiaohua Wang, Zhenghua Wang, Xuan Gao, Feiran Zhang, Yixin Wu, Zhibo Xu, Tianyuan Shi, Zhengyuan Wang, Shizheng Li, Qi Qian, Ruicheng Yin, Changze Lv, Xiaoqing Zheng and Xuanjing Huang. 2024. Searching for Best Practices in Retrieval-Augmented Generation. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{wang2024searching,
  title = {{Searching for Best Practices in Retrieval-Augmented Generation}},
  author = {Xiaohua Wang, Zhenghua Wang, Xuan Gao, Feiran Zhang, Yixin Wu, Zhibo Xu, Tianyuan Shi, Zhengyuan Wang, Shizheng Li, Qi Qian, Ruicheng Yin, Changze Lv, Xiaoqing Zheng and Xuanjing Huang},
  booktitle = {Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing},
  year = {2024},
  doi = {10.18653/v1/2024.emnlp-main.981},
  url = {https://doi.org/10.18653/v1/2024.emnlp-main.981}
}
```

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*Updated 1 November 2026*