Should I Prioritize Recall or Precision in Retrieval-Augmented Generation (RAG) Pipelines?

# Should I Prioritize Recall or Precision in RAG Pipelines?

## Short Description

This report reviews recent research on the trade-offs and impacts of prioritizing recall versus precision when building Retrieval-Augmented Generation (RAG) pipelines. RAG combines retrieval of relevant documents with generative models and is widely used in question answering and other NLP applications that require external knowledge integration.

## Summary of Work

Recent papers emphasize hybrid and graph-enhanced retrieval mechanisms that improve both recall and precision simultaneously. For example, BifrostRAG introduces a dual-graph retrieval structure for multi-hop QA, achieving around 92.8% precision and 85.5% recall, showing that balancing both can significantly enhance results.

Another study on enterprise data RAG systems combines dense embeddings with BM25 and metadata-aware filtering to boost both precision and recall notably, indicating the benefit of hybrid and multi-strategy retrieval.

Temporal reasoning in graph-based RAG frameworks like DyG-RAG improves recall on complex queries by modeling event sequences, showing that recall is crucial for complex, multi-hop retrieval but precision is maintained through structured event representations.

A visual analysis tool for graph-based RAG, XGraphRAG, focuses on identifying critical recall points and tracing recalls in the system to enhance reliability and debugging, highlighting the need to monitor and balance recall and precision interactively.

Overall, recall is often important for ensuring the retrieval of all potentially relevant information, especially in multi-hop or complex queries, while high precision is critical to avoid noise and maintain the quality of generated outputs. The balance depends on the application: 
- For compliance and safety critical use cases, precision might be prioritized to minimize incorrect facts.
- For exploratory or multi-hop question answering, recall may be more critical to ensure comprehensiveness.

## Papers

1. **BifrostRAG: Bridging Dual Knowledge Graphs for Multi-Hop Question Answering in Construction Safety** (arXiv:2507.13625)
   - Hybrid graph and vector retrieval for high precision and recall in complex QA.

2. **Advancing Retrieval-Augmented Generation for Structured Enterprise and Internal Data** (arXiv:2507.12425)
   - Hybrid retrieval strategies combining dense retrieval and BM25 with filtering improve both recall and precision.

3. **DyG-RAG: Dynamic Graph Retrieval-Augmented Generation with Event-Centric Reasoning** (arXiv:2507.13396)
   - Event graph and temporal retrieval improve recall for time-sensitive temporal QA.

4. **XGraphRAG: Interactive Visual Analysis for Graph-based Retrieval-Augmented Generation** (arXiv:2506.13782)
   - Framework for analyzing recall and precision trade-offs in graph-based RAG systems.

# Recommendation

When building RAG pipelines, prioritize recall if the use case demands comprehensive information coverage, such as multi-hop or exploratory QA. Prioritize precision if the application requires high factual accuracy and minimal noise, such as compliance or safety-related tasks. Using hybrid retrieval methods and graph-based approaches can help balance recall and precision effectively.

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This gist provides an overview of the current research insights on recall vs precision in RAG pipelines with references to recent studies for deeper exploration.