Issue #115 - Reranking in your RAG pipeline

2025-12-14 · Source: Machine Learning Pills · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

This article addresses the "Day 2" problem in Retrieval-Augmented Generation (RAG) pipelines, where initial prototypes work well but struggle with complex user queries due to inefficient document retrieval. It introduces reranking as a solution to improve the precision of retrieved documents, bridging the gap between fast vector search and intelligent contextual understanding. The proposed approach uses a two-stage pipeline: an initial retrieval stage (e.g., Hybrid Search combining Vector and Keyword Search) to cast a wide net and gather a "candidate set" of documents, followed by a reranking stage. In the reranking stage, a Cross-Encoder model, such as "BAAI/bge-reranker-base," re-evaluates and reorders the top 50 candidate documents, pushing the most relevant ones to the top for the Large Language Model (LLM) to process. The article includes a full code implementation using Python and LangChain, demonstrating how to set up a `PubMedRetriever` and integrate a `CrossEncoderReranker`.

Key takeaway

For AI Engineers building RAG systems that experience "Day 2" performance issues with complex queries, you should integrate a reranking step into your pipeline. This improves the quality of documents fed to the LLM, reducing hallucinations and "I don't know" responses. Consider using a Cross-Encoder model like `BAAI/bge-reranker-base` with LangChain to refine your retrieval results, ensuring the LLM receives the most pertinent information.

Key insights

Reranking enhances RAG pipeline precision by reordering initially retrieved documents using a Cross-Encoder.

Principles

• Balance retrieval speed with precision.
• LLMs perform better with highly relevant context.
• Two-stage retrieval improves RAG accuracy.

Method

Implement a two-stage RAG pipeline: first, use a broad retriever (e.g., Hybrid Search) to generate a candidate set, then apply a Cross-Encoder reranker to reorder and select the most relevant documents for the LLM.

In practice

• Use `BAAI/bge-reranker-base` for reranking.
• Set `top_k_results` high for initial retrieval.
• Configure `CrossEncoderReranker` to `top_n` for final selection.

Topics

• RAG Pipeline
• Reranking
• Cross-Encoder
• Vector Search
• Hybrid Search

Best for: AI Engineer, Machine Learning Engineer, AI Student

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• What No RAG Tutorial Tells You Before You Start Building — Effective RAG implementation hinges on robust retrieval strategies, not just LLM capabilities, to prevent confident but incorrect answers.
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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning Pills.