In this article, you will learn:
- Why initial retrieval methods like vector search and BM25 fail to capture true relevance
- How bi-encoders and cross-encoders differ architecturally and when to use each
- The key tradeoffs between speed and accuracy in reranking approaches
- Benchmark data showing real-world reranking performance gains in agent pipelines
- When reranking justifies the latency overhead in your pipeline
- Practical implementation guidance for integrating rerankers into RAG workflows
Let's dive in!
Why Reranking Matters in RAG Pipelines
In a typical RAG pipeline, the retriever returns the top K candidate passages based on a query. The problem is that the retriever's notion of "similarity" is fundamentally limited. It misses truly relevant passages and includes loosely related ones. Understanding why this happens reveals why reranking is essential.
In fact, it’s common to find that the most relevant document might be ranked lower than the top K cutoff due to this information loss. Simply raising K (to retrieve more) isn’t a cure-all, because we can’t stuff an unlimited number of documents into an LLM’s context window without overwhelming it or exceeding token limits.
Reranking breaks this tradeoff with a two-stage approach:
- Retrieve broadly: Pull a larger initial set of candidates (50-100 documents) to maximize recall of potentially relevant information
- Rerank precisely: Use a sophisticated reranker to score each document's relevance to the query, then select only the top few (3-5) to send to the LLM
The reranker acts as a high-precision judge, understanding the fine-grained relationship between query and document. It bridges the gap between retrieval recall (casting a wide net) and LLM precision (providing focused, high-quality context). By filtering out the noise before it reaches the LLM, reranking ensures the most relevant information lands in those critical first and last positions.
This becomes especially critical in AI web search applications where agents need to extract accurate information from vast amounts of online content. When your AI searches the web for real-time data or current events, initial retrieval methods may return hundreds of loosely related pages. Without reranking, your AI agent might answer questions based on tangentially related search results rather than the most relevant ones.
For instance, a query about recent semiconductor export regulations could return general semiconductor news, company earnings reports, and historical policy changes. A reranker cuts through this noise to surface the exact policy documents and expert analyses your agent needs, dramatically improving the accuracy of extracted information and the quality of final answers.
Tools like Bright Data's SERP API provide structured search results from Google, Bing, and other engines that feed directly into this retrieve-and-rerank pipeline, ensuring your initial candidate set is both comprehensive and well-formatted. This is why reranking has become essential infrastructure for production AI systems that depend on web search and RAG pipelines.
How Bi-Encoders and Cross-Encoders Differ
Modern reranking models generally fall into two broad categories: bi-encoders and cross-encoders. Both can rank documents by relevance to a query, but they operate very differently under the hood, with distinct trade-offs in speed and accuracy. Understanding the architectural differences helps you choose the right tool for your use case.
Bi-Encoders: Fast but Limited
A bi-encoder consists of two twin models that encode the query and document independently into vector embeddings. Relevance is computed via a quick similarity function (e.g. cosine similarity or dot product) between these vectors. This approach is extremely scalable – you can pre-compute embeddings for millions of documents, store them in a vector index, and at query time only encode the query and retrieve nearest neighbors in milliseconds.
Bi-encoders are the workhorses of dense retrieval because they enable efficient large-scale search. However, because the query and document are encoded separately, the model cannot directly account for fine-grained interactions between specific query terms and document text.
The result is that bi-encoders sometimes miss nuances; their ranking is based on coarse semantic similarity, which can lead to slightly lower precision in matching the exact information need.
Cross-Encoders: Accurate but Slow
A cross-encoder directly addresses the nuance problem by jointly modelling the query and document. It feeds the concatenated text pair into a transformer model (e.g., BERT) and outputs a relevance score (typically as a classification or regression).
This means the model can perform full self-attention between every query token and every document token, allowing it to capture subtle interactions among terms, word order, negation, and context dependencies that a bi-encoder would gloss over.
Cross-encoders are far more accurate at predicting relevance because they effectively “read” the document with respect to the query. In fact, if trained on a representative dataset, a cross-encoder often achieves substantially higher ranking metrics than a bi-encoder on the same task.
Why does the cross-encoder tend to outperform the bi-encoder?
The core reason is that a bi-encoder must compress an entire document’s meaning into a single vector before seeing the query. This compression loses detail and cannot account for the user's actual request.
A cross-encoder, by contrast, can examine the document in light of the specific query, with full access to all token-level interactions (thanks to the transformer's attention mechanism).
Late Interaction Models: The Middle Ground
Late interaction models like ColBERT offer a compromise between bi-encoder speed and cross-encoder precision. Instead of creating a single vector per document, ColBERT generates token-level embeddings for both query and document, then computes relevance through efficient token-to-token matching (MaxSim operation).
How ColBERT works:
- Encode query into N token embeddings
- Encode document into M token embeddings
- For each query token, find its maximum similarity to any document token
- Sum these maximum similarities as the final relevance score
Advantages
- More nuanced than bi-encoders (token-level interactions)
- Faster than cross-encoders (no full attention mechanism)
- Can pre-compute and cache document embeddings
Tradeoffs
- Requires more storage than bi-encoders (token-level vs. document-level embeddings)
- Still slower than pure bi-encoder retrieval
- More complex indexing infrastructure
Benchmark Results: Real-World Reranking Performance
Here's concrete data from published research showing how reranking improves retrieval quality and answer accuracy in production systems.
Cross-Encoder Performance: MS MARCO Benchmarks
Research from the Sentence Transformers project evaluated multiple cross-encoder models on the TREC Deep Learning 2019 and MS MARCO datasets. These are industry-standard benchmarks for passage reranking.
Model Performance on TREC DL 2019 and MS MARCO
| Model | NDCG@10 (TREC DL 19) | MRR@10 (MS MARCO) | Throughput (docs/sec) |
|---|---|---|---|
| cross-encoder/ms-marco-MiniLM-L6-v2 | 74.30 | 39.01 | 1,800 |
| cross-encoder/ms-marco-MiniLM-L12-v2 | 74.31 | 39.02 | 960 |
| cross-encoder/ms-marco-TinyBERT-L2-v2 | 69.84 | 32.56 | 9,000 |
| BM25 (baseline) | 45.46 | - | - |
Key findings
- Cross-encoder reranking improved NDCG@10 by 63% over BM25 baseline (45.46 to 74.30)
- Even the smallest model (TinyBERT-L2) achieved 54% improvement while processing 9,000 docs/second.
- The performance ceiling approaches 95.47 NDCG@10, indicating substantial room remains for improvement.
Source: Sentence Transformers MS MARCO Cross-Encoders documentation, models evaluated on V100 GPU
ColBERT Late Interaction: BEIR Benchmark Results
ColBERT models offer a middle ground between bi-encoders and cross-encoders. Research from Mixedbread AI and Jina AI evaluated ColBERT performance across 13 BEIR datasets.
Reranking Performance (BM25 first-stage retrieval, NDCG@10)
| Model | Average NDCG@10 | Speed vs Cross-Encoder |
|---|---|---|
| mxbai-colbert-large-v1 | State-of-the-art | 3-5x faster |
| Jina-ColBERT-v1 | Comparable to cross-encoders | 3-5x faster |
| ColBERTv2 (baseline) | Strong performance | 3-5x faster |
Domain-Specific Results
- Scientific documents (SciFact): NDCG@10 > 70% for multiple models
- Challenging domains (Scidocs): NDCG@10 peaked at 10.4%, showing dataset difficulty variance
- Long-context documents: Jina-ColBERT outperformed ColBERTv2 due to 8k context window
Source: Mixedbread AI blog, Jina AI HuggingFace model cards, BEIR benchmark evaluations
When (and When Not) to Use Rerankers
Given their advantages, one might wonder if rerankers are always necessary. The truth is, rerankers add the most value when your initial retrieval is under pressure – for example, when dealing with a large corpus, difficult queries, or high-precision requirements:
Large Corpus, Broad Recall
If you have tens of thousands or millions of documents, an initial bi-encoder or BM25 retrieval will inevitably return some loosely related results alongside relevant ones. A reranker is valuable to filter semantic noise from a big candidate set. It ensures that even if the first-stage retrieval is generous (high recall), the final documents passed to the LLM are precise. Without reranking, you might pass many irrelevant chunks to the LLM, risking confusion and error.
Learn how to build production-ready agentic RAG systems
Complex or Ambiguous Queries
Queries with subtle semantics, negations, or multiple conditions (e.g. “history of startup not acquired by Google” or multi-part questions) often stump simple retrievers. A reranker that considers context can infer what the user really wants.
For instance, with a query “RAG without a vector database,” a cross-encoder can interpret the “without” clause and prefer documents about non-vector solutions. In contrast, a bi-encoder might just grab anything about RAG and vector DBs. Cross-encoders especially excel at these nuanced cases, as they consider the full query-document interplay.
High-Stakes Domains
In domains such as law, medicine, and finance, the accuracy of information is paramount, and queries can be highly specific. Here, precision is non-negotiable – you’d rather miss a possibly relevant document than include a wrong one that could mislead the answer.
Rerankers (and even multiple reranking stages) are often used in such systems to ensure only the most authoritative and relevant texts are considered. The slower speed is justified by the need for accuracy (and, in some cases, the document sets may be smaller, making cross-encoder costs manageable).
Getting Started with Reranking
For most production systems, start with ms-marco-MiniLM-L-6-v2 (open-source, fast) or Cohere Rerank v3.5 (API, highest quality) and measure impact before optimizing further.
Before implementing reranking, ensure you have quality data sources. For web-based RAG systems, structured SERP APIs like Bright Data's provide cleaner input than raw web scraping, which improves both retrieval and reranking performance. Well-formatted data from Google, Bing, and other search engines creates a stronger foundation for the entire pipeline.
The implementation checklist:
- Retrieve 50-100 candidates from your vector store
- Apply reranking to reduce to top 10-20
- Track NDCG/MRR alongside downstream answer quality
- Measure latency impact and adjust candidate counts accordingly
- Consider domain-specific fine-tuning if general models underperform
The tooling has matured. The two-stage retrieve-then-rerank pattern is now standard practice. The main question is not whether to rerank, but which model fits your latency and accuracy requirements.
Building Better Search for AI Agents
Reranking is just one piece of the puzzle. When your AI agents need to search the web, scrape data, or access real-time information, having reliable data infrastructure matters.
Bright Data's SERP API provides structured search results from Google, Bing, and other engines that can feed directly into your retrieval pipeline. Combined with proper reranking, you get both comprehensive coverage and precision ranking.
For teams building RAG systems that pull from web sources, the combination of quality data collection and smart reranking delivers the best results. Start with the fundamentals: get good data in, rank it well, and send only the most relevant context to your LLM.