In Retrieval-Augmented Generation (RAG) systems, the process of information retrieval is the foundation, but the mere ability to find documents matching the user’s query is not always enough. A crucial stage that determines the final quality of the answer is reranking – the process of re-evaluating and reordering search results. Reranking allows the system to select the most relevant results from those initially identified by the retrieval system. Although it may sound simple, in practice it requires advanced algorithms, proper modeling, and thoughtful optimization.

In this article, we will examine how reranking works, what challenges are associated with its implementation, and how it affects the time, quality, and costs of operating a RAG system.

What is reranking?

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Reranking is the process of sorting search results according to their relevance to the user’s query. In a classic RAG pipeline, it looks like this:

• Retrieval: The algorithm searches the index for documents most relevant to the query.
• Reranking: The results are evaluated by a more advanced model, which prioritizes and reorders them.

The models used for reranking analyze both the content of the documents and the context of the query. These can range from simple rule-based algorithms to advanced machine learning models, such as BERT or other transformer networks.

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Why is reranking important?

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Without reranking, search results depend on the basic retrieval algorithm, which often relies on simpler methods such as:

• BM25 (Bag of Words): Evaluates results based on the number of shared words between the query and the document.
• Dense retrieval: Uses embedding vectors to compare the query with documents.

Although these methods are fast and effective, they may not capture more subtle linguistic and contextual aspects. Reranking makes it possible to improve results by considering factors such as:

• Understanding the intent behind the query.
• The semantic relationship between the query and the document.
• The importance of the entire context, not just individual keywords.

Models and Methods of Reranking

1. Heuristics and Business Rules
The simplest approach to reranking is to apply business rules or heuristics, such as:

• Preferring documents from specific categories.
• Considering the publication date (newer documents may be more relevant).

Advantages: fast and inexpensive to implement.
Disadvantages: limited flexibility and accuracy in complex cases.

2. NLP-based Models
Advanced models such as BERT can understand the context and meaning of the query and document content.

Example of using BERT:
The BERT model can analyze fragments of document text and assess their relevance to the query. Results are then sorted based on the predicted probability that the document meets the user’s intent.

Advantages: high accuracy and context understanding.
Disadvantages: high computational demand, which may increase costs.

3. Hybrid Approach
In many cases, the best solution is to combine simple methods with advanced models.

• The initial retrieval stage (e.g., BM25 or dense retrieval) extracts the top 100–200 documents.
• Reranking is applied to a smaller set of documents (e.g., top 10–20), which reduces the system’s load.

Challenges in Implementing Reranking

1. Balancing Quality and Response Time
Advanced reranking models such as BERT can improve accuracy, but their processing time may be too high for real-time systems. The solution is to limit the number of documents subjected to reranking.

2. Domain Adaptation
General-purpose models do not always perform well in specific fields. In such cases, fine-tuning the model on domain-specific data is necessary.

3. Costs
Computational costs increase with the use of advanced reranking models. Optimization requires a compromise between system performance and operational costs.

Reranking in Practice: Example from X-TALK

In one of X-TALK’s projects, we had to deal with the problem of low search result accuracy in a customer service system. After implementing the RAG pipeline, we noticed that retrieval results (dense retrieval based on embeddings) were not precise enough for complex user queries.

Our solution:

• The initial retrieval stage extracted the top 50 documents.
• On this set, we applied reranking based on the BERT model, which analyzed the full query and document fragments.
• The final results were sorted by relevance as predicted by the model.

Effect:

• Search result accuracy increased by 35%.
• Response time increased only slightly thanks to limiting the number of documents reranked.

When Is Reranking Worth Using?

Reranking is particularly useful in cases such as:

• Complex queries requiring thorough context analysis.
• Customer service systems, where answer quality impacts user satisfaction.
• Highly variable data, where simple retrieval methods fail.

Summary

Reranking is an indispensable element of advanced RAG systems that significantly improves answer accuracy. Although it comes with challenges such as higher computational resource demands, its application can determine the success or failure of the entire system.

At X-TALK, we implement reranking in an optimized way, balancing quality and performance, which allows us to deliver solutions that meet the high expectations of our clients.

If you plan to implement RAG in your organization, remember that reranking is a crucial element worth planning with an experienced team.