Reranking in RAG: How It Works, Types, Models, and Tools

Quick Overview:

• Reranking in RAG reorder the first retrieved documents based on their relevance to a user’s query, helping the LLM to receive more useful information before generating a response.

• Common reranking methods include cross-encoder, LLM-based, pointwise, pairwise, listwise, and late-interaction reranking. 

• Popular reranking models include Cohere Rerank, BGE Reranker, Jina Reranker, MS MARCO MiniLM Cross-Encoder, and Qwen3-Reranker. 

• In this blog you will learn what is reranking in RAG along with how it works, different types, popular reranking models and tools, and how to select among them.

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What Is Reranking in RAG?

Retrieval-Augmented Generation (RAG) systems collect information from the external sources before sending it to the Large Language Model (LLM). However the first search by the RAG does not always return the most useful information, some reults may be releated to the query without directly answering it.

The reranking in RAG helps to solve this problem. It checks the documents retrieved during the first search and re order them according to their relevance to the user’s question. 

Let’s understand this through reranking in rag example and everyday analogy below:

In an interview, during the first round, the recruiter select candidates from who are eligible for the role. During the second round, the recruiter examines their experience, skills, and some more criteria closely.

Reranking in RAG follow the same process. The initial retriever finds potentially relevant documents, while the reranker takes a closer look and selects the strongest matches.

Why Does Reranking Matter?

Reranking in RAG helps you by:

• Improving the relevance of retrieved documents.

• Moving the most useful passages closer to the top.

• Reducing irrelevant context sent to the LLM.

• Improving the quality of answers in document-based question-answering systems.

• Helping hybrid retrieval systems combine and refine results.

• Making better use of the LLM's available context window.

It is important to note that the reranking does not guarantee accurate answers. It also cannot recover information that the initial retriever failed to find.

Also Read: Vector Database: Architecture, Indexing, Search, and Use Cases

How Does Reranking Work in RAG?

Reranking in RAG works by the user’s query and initial search and end with the generated answer.

The workflow looks like this:

Let's understand each step through a reranking in RAG example.

Step 1: Receive the User's Query

Suppose a user asks: "How can I reset my company's login password?"

The RAG system receives the question and prepares it for retrieval.

Step 2: Retrieve Candidate Documents

The initial retriever searches the knowledge base using vector similarity, keyword matching, or a combination of methods.

It might return these five documents:

The documents are related to accounts, but they do not all answer the question equally well.

Step 3: Score the Documents

The reranking model is used to analyze the user’s query and each document separately. The cross-encoder works by analyzing the user’s query and the document at once and estimating their relationship. 

This helps the model to analyze the factors that are not analyzed when ranking search results independently.

Also Read: Gradient Boosting Algorithm in Machine Learning: How It Works, and Practical Examples

Step 4: Reorder the Results

After scoring the documents, the reranker may produce a new order.

Step 5: Pass the Best Results to the LLM

In the RAG pipeline, the most relevant pieces are chosen and included into the prompt.

The LLM can produce a response using the retrieved instructions rather than getting several documents that may not be fully related to the task.

Different Types of Reranking Methods in RAG

RAG systems use different reranking methods such as Cross-Encoder, LLM-Based, Pairwise etc.   to improve the order of retrieved documents. 

Let’s explore each of them in detail:

1. Cross-Encoder Reranking

A cross-encoder considers both the user query and the document. The relevance score is generated depending on whether the document fulfills the user’s query or not.

Example: For the query "How do I reset my password?", the model may give these scores:

The system ranks the password reset guide first because it has the highest score.

Also Read: Support Vector Machine (SVM) in Machine Learning: Algorithm, Types, Kernels, and Examples

2. LLM-Based Reranking

LLM-based reranking assesses the relevancy of the document. In this technique, the scores could be generated for documents, a pair comparison could be done, or the ranking could be established on the basis of relevancy.

Example: A legal research assistant retrieves 20 case summaries. The LLM reviews them and places the 5 most relevant summaries at the top.

3. Pointwise Reranking

In pointwise reranking, the document is individually assessed to assign the relevancy score. Then, the documents are ranked based on the relevancy score.

Example: A system evaluates 10 documents one by one. A password recovery guide gets a score of 0.92, while a profile settings guide gets 0.40.

The system ranks the first document higher.

4. Pairwise Reranking

Pairwise reranking includes comparing two documents at once and choosing the one that answers the query of the user.

Example: A system compares 2 articles about account access. If Article A is more relevant than Article B, it ranks Article A higher. It repeats this process with other pairs to create a ranked list.

Also Read: Decision Tree Algorithm in Machine Learning: How It Works, Types, and Examples

5. Listwise Reranking

Listwise re-ranking involves ranking a group of documents together based on their relevancy. This method takes into consideration more than one document simultaneously without considering each document individually.

Example: A system receives 10 retrieved documents and ranks them from position 1 to position 10. The most relevant document appears at position 1.

6. Late-Interaction Reranking

Late-interaction models compare individual query terms with document terms. This helps the system identify detailed matches that a single vector may miss.

ColBERT is a well-known example of this approach.

Example: A user searches for "Error 404 in Python API". The model checks how terms such as "404", "Python", and "API" match the content of each document.

Let’s explore the most popular reranking models and tools used in RAG pipelines.

Popular Reranking Models and Tools for RAG

Several models and frameworks such as Cohere Rerank, BGE Reranker, Qwen3-Reranker  support reranking in RAG. Below are some of the most popular reranking in RAG models:

Popular Reranking Models

Tools for RAG Reranking

Now you are at the point where reranking is clear to you, now let's know how to choose the right reranking model according to your choice.

Also Read: Logistic Regression in Machine Learning: Algorithm, Types, Examples, and Applications

How to Choose the Right Reranking Model

Choosing a reranking model involves more than comparing model names. Below are some factors on which you can compare the different models:

Conclusion

From the above you now know that Reranking in Retrieval Augmented Generation ensures that the order of retrieved documents becomes better. Through the analysis of the relevance between the query and document, the Reranker will ensure the retrieval of better context to be used in response generation by the LLM.

There are different cross-encoders, rerankers based on LLM, point-wise approach, list-wise approach, and late interaction approaches which have various strengths and weaknesses.