In retrieval-augmented generation (RAG) systems, it is common to use a fast retriever to fetch a larger set of candidates (e.g., top 20, 50, or 100) and then apply a slower but more accurate reranking model to score and reorder those candidates against the user question.

Verified by Lenz · May 7, 2026

The Short Version

The evidence strongly supports this as a common RAG design pattern. Across academic and industry sources, systems frequently use a fast first-stage retriever to gather a broader candidate set and then a slower, more accurate reranker to reorder results against the query. The example candidate sizes are consistent with reported practice.

Caveats

'Common' does not mean universal; many simpler or latency-sensitive RAG systems skip reranking.
Candidate-set sizes vary by system and workload; 20, 50, and 100 are examples, not a fixed standard.
Reranking usually improves relevance but adds compute cost and latency, so deployment choices depend on the accuracy-speed tradeoff.

The Receipts

Rerankers and Two-Stage Retrieval
Pinecone
What are Rerankers?
MongoDB
Enhancing RAG Pipelines with Re-Ranking
NVIDIA Technical Blog
Comparative Analysis of Neural Retriever-Reranker Pipelines for Retrieval-Augmented Generation
arXivFeb 28, 2025
Understanding the Fundamental Design Decisions of Retrieval-Augmented Generation Systems
arXivDec 2, 2024
Top 5 Reranking Models to Improve RAG Results
MachineLearningMastery
How Using a Reranking Microservice Can Improve Accuracy and Costs of Information Retrieval
NVIDIA DeveloperSep 10, 2025
Advanced RAG Retrieval: Cross-Encoders & Reranking
Towards Data Science
A complete guide to RAG evaluation: metrics, testing and best practices
Evidently AI
RAG Reranking Explained: How To Improve RAG Results
YouTube

+ 15 more sources — see the full list on Lenz

Filed Under

RerankingRetrieval-Augmented Generation