What is Reranking?

What Is Reranking?

Reranking is a technique used in AI search and retrieval systems to improve the quality of search results after they have been initially retrieved. Instead of relying solely on the first-pass search to determine which results are most relevant, a reranking model takes the initial results and re-evaluates each one against the original query, producing a more accurate ordering of results by relevance.

Consider a practical analogy. Imagine you ask your assistant to pull every document related to "market entry strategy for Vietnam" from your filing system. Your assistant quickly gathers 20 potentially relevant documents based on titles and keywords. Reranking is like having a senior analyst then read through those 20 documents and reorder them based on which ones are genuinely most useful for your specific question, rather than just superficially related. The senior analyst brings deeper understanding and judgement that the initial filing system search could not provide.

In technical terms, reranking uses a specialised AI model, called a cross-encoder, that reads both the query and each retrieved document together, evaluating their relevance as a pair. This produces much more accurate relevance scores than the initial search, which typically evaluates the query and documents separately.

How Reranking Works

Reranking fits into the retrieval pipeline as a second stage that refines the results from the first stage:

• Stage 1 — Initial retrieval: A fast search method, typically vector search, keyword search, or hybrid search, retrieves a broad set of potentially relevant documents. This stage prioritises speed and recall, casting a wide net. It might return 50 to 100 candidate documents.
• Stage 2 — Reranking: A more powerful but slower model evaluates each candidate document against the original query. Unlike the initial search, which compares pre-computed representations, the reranking model reads the query and each document together as a pair, allowing it to understand nuanced relevance signals like context, negation, and specificity. The documents are then reordered based on these refined relevance scores.
• Stage 3 — Selection: The top-ranked documents from the reranking step, typically the top 3 to 10, are passed to the large language model as context for generating a response.

Popular reranking models include Cohere Rerank, Jina Reranker, cross-encoder models from the sentence-transformers library, and reranking capabilities built into platforms like Pinecone and Weaviate. Many of these can be deployed as API services, requiring minimal engineering effort to integrate.

The reason reranking is more accurate than initial retrieval is fundamentally architectural. Initial search methods encode the query and documents independently, then compare their representations. Reranking models process the query and document together, allowing them to capture subtle interactions between the two. This is slower, which is why it is used only on the pre-filtered set of candidates rather than the entire document collection.

Why Reranking Matters for Business

For business leaders, reranking addresses a specific and costly problem: the gap between what your AI search system retrieves and what is actually most relevant to the question being asked.

• Higher quality AI responses. In RAG systems, the language model generates its answer based on the documents provided to it. If the top-retrieved documents are only loosely relevant, the AI's response will be vague, incomplete, or inaccurate. Reranking ensures the AI receives the most genuinely relevant documents, leading to noticeably better answers.
• Reduced hallucination. When an AI model receives marginally relevant context, it is more likely to fill gaps with fabricated information. By providing precisely relevant documents through reranking, you reduce the surface area for hallucination, a critical concern for business applications where accuracy matters.
• Better user experience. Whether your AI system serves internal employees searching a knowledge base or external customers using a support chatbot, the quality of results directly affects user trust and adoption. Reranking can be the difference between an AI tool that employees embrace and one they abandon after a few frustrating experiences.
• Cost efficiency. Rather than building a more expensive and complex initial retrieval system, adding a reranking step is often a simpler and more cost-effective way to improve result quality. It works as a bolt-on improvement to your existing search infrastructure.

Research and industry benchmarks consistently show that adding reranking to a RAG pipeline improves answer quality by 10 to 25 percent on standard evaluation metrics, making it one of the highest-impact, lowest-effort improvements available.

Key Examples and Use Cases

Financial advisory. Wealth management firms in Singapore serving high-net-worth clients use AI systems to retrieve relevant market research and product information. When a relationship manager asks about "fixed income opportunities in emerging Asian markets," the initial search might retrieve documents about Asian equities, global fixed income, and emerging market currencies. Reranking ensures that the documents specifically addressing fixed income in emerging Asian markets are prioritised, enabling the AI to give a focused, accurate response.

Healthcare information systems. Hospital groups across Southeast Asia are deploying AI-powered clinical reference tools. When a doctor searches for treatment protocols, reranking ensures that the most clinically relevant and current guidelines appear first, rather than tangentially related documents. The precision of results in healthcare contexts can have direct patient safety implications.

Legal research. Law firms in the region processing case law and regulatory documents need highly precise retrieval. A search for "data protection obligations for cross-border transfers under PDPA" should prioritise documents specifically about the Personal Data Protection Act's cross-border provisions, not general data protection overviews. Reranking makes this level of precision achievable.

Enterprise search for conglomerates. Large Southeast Asian conglomerates like Sinar Mas, Salim Group, or Ayala Corporation operate across diverse industries. When executives search internal knowledge bases for strategic information, reranking ensures results are relevant to the specific business context of the query rather than just topically similar documents from unrelated business units.

Getting Started with Reranking

Adding reranking to an existing RAG system is one of the most straightforward and impactful improvements you can make:

1. Assess your current retrieval quality. Before adding reranking, measure the relevance of your current top results. If your initial retrieval already returns highly relevant documents, reranking will have less impact. If the right information is in your retrieved set but not always at the top, reranking will help significantly.
2. Choose a reranking solution. For the fastest implementation, use an API-based service like Cohere Rerank or the reranking features built into your vector database. For more control and lower per-query costs at scale, deploy an open-source cross-encoder model.
3. Retrieve broadly, then rerank. Configure your initial retrieval to return 20 to 50 candidates rather than just the top 5. This gives the reranker a larger pool to work with, increasing the chance that the most relevant documents are included.
4. Measure the improvement. Compare the quality of your AI system's responses before and after adding reranking using a consistent test set of questions. Track both automated metrics and user satisfaction.
5. Monitor latency. Reranking adds processing time to each query, typically 100 to 300 milliseconds for API-based services. Ensure this additional latency is acceptable for your use case and user expectations.

Reranking is increasingly recognised as an essential component of production RAG systems. For any organisation that has deployed a RAG-based AI application and wants to meaningfully improve its accuracy without a major architectural overhaul, reranking should be the first optimisation to consider.