Rerankers and Search Models: The Key to Better RAG Results

In the world of generative AI and building smart applications, Retrieval-Augmented Generation (RAG) has become the gold standard. The principle is simple: instead of hoping that a Large Language Model (LLM) knows all the facts by heart, you first provide the model with the right documents via a search query. Yet, many developers notice that their RAG systems falter in practice. The answers are sometimes irrelevant, or the correct information is just missing from the documents sent to the LLM.

The culprit is often the search process itself. If the search engine cannot find the relevant text fragments, the language model cannot formulate a good answer either. This is where rerankers and specialized search models come into play. In this article, we explain why a simple vector search often falls short, how a reranker solves this problem, and when it pays off to add this extra, often computationally intensive, step to your architecture.

How do search models work in a RAG pipeline?

A standard RAG pipeline usually works with a 'bi-encoder'. This is a neural network that converts texts into rows of numbers, or embeddings. Both the documents in your database and the user's search query are passed through this model. Search is then performed by looking at which sequences of numbers (vectors) are closest to each other in a mathematical space. This process is called 'vector search' or 'semantic search'.

While this is incredibly fast and scales to millions of documents, it lacks a deep understanding of the text. Because the search query and the document are converted into numbers independently of each other, there is no way for the model to see exactly how the words from the search query interact with the words in the document. As a result, documents that are globally about the same topic are often ranked highly, even if they do not answer the specific question.

The Difference Between Embedding Models and Rerankers

To solve this problem, the search process is increasingly divided into two phases: a fast, broad search query using embeddings, followed by a slow, highly precise filtering with a reranker. Also check out our guide comparing the different embedding models compared to see which base models perform best.

Phase 1: Embedding and Vector Search (Bi-Encoders)

The first phase is designed for speed and scalability. A bi-encoder pre-computes the embeddings for all your documents. When a user asks a question, only that question is converted into an embedding. The database then rapidly calculates the mathematical distance (for example, 'cosine similarity') between the question and all documents. Out of millions of options, you retrieve the top 100 most promising documents in milliseconds. The downside? Precision is low. The model does not fully grasp the nuances.

Phase 2: Reranking (Cross-Encoders)

This is where the reranker (usually a so-called 'cross-encoder') comes into play. Instead of comparing pre-computed vectors, a cross-encoder inputs the search query and the document simultaneously. Through its 'attention mechanism', the model can directly see how each word in the query relates to each word in the document. It essentially asks itself: "Does this specific text answer this specific question?"

The result is a relevance score per document. Because the cross-encoder must re-analyze the top 100 documents from phase 1 one by one relative to the query, this process is computationally intensive and slower. However, the major advantage is a spectacular increase in the quality of the top 5 documents.

Why a Reranker Drastically Improves Your Results

You might wonder if that extra step is worth the effort. In practice, a reranker often makes the difference between an experimental AI chatbot and a usable production system. There are two main reasons why rerankers are crucial for high RAG quality.

            Contextual Nuance: A user searches for "How do I cancel my flight due to illness?". A standard vector search might find the document "Conditions for canceling a flight by the airline". Semantically, the words are very close (canceling, flight). A reranker recognizes the nuance ("due to illness" and the customer's perspective) and will rank a document about "Illness and passenger cancellation insurance" much higher.
        

Additionally, a reranker helps optimize what we send to the generative LLM. Language models struggle to process massive amounts of text, a phenomenon known as 'Lost in the Middle' (read more about this in our context window explanation). If you simply send the top 20 documents from your vector search to the LLM in the hope that the correct answer is in there somewhere, the model gets confused or ignores important pieces of text in the middle. A reranker allows you to confidently send only the top 3 or 5 most relevant documents to the LLM. Less noise leads to better, hallucination-free answers.

Latency and Costs: When Does the Extra Step Pay Off?

Adding a cross-encoder to your architecture is not free. There are clear trade-offs in terms of speed (latency) and infrastructure costs. If you are considering the step to production, we recommend also reading our analysis on the Total Cost of Ownership between open and closed models.

Because a reranker must process each document in combination with the search query live, search time increases. While a vector search is often completed in 20 to 50 milliseconds, a reranking step (where you re-score the top 50 documents, for example) can easily take 200 to 800 milliseconds, depending on the computing power (CPU vs GPU) and the size of the reranking model. If you use external APIs, such as Cohere's popular Rerank endpoint, you also pay per input token for the reranking task.

The extra latency and costs almost always pay off in complex domains such as legal documents, medical questions, and customer service, where precision is essential. However, if you are building a simple internal search engine for general manuals where error margins are acceptable, a well-tuned embedding model with only vector search is often sufficient.

How Do You Measure the Effectiveness of a Reranker?

To avoid making architectural choices purely based on gut feeling, it is important to quantify the impact of a reranker. Within the 'Information Retrieval' (IR) world, there are proven metrics to objectively assess the quality of search results.

Hit Rate (or Recall@K): Measures how often the correct document is within the top K results you send to your LLM (for example, in the top 5). If a reranker increases your Recall@5 from 60% to 85%, that is a massive gain for your RAG system.
MRR (Mean Reciprocal Rank): This metric looks not only at whether the correct document was found, but how high it ranks in the list. The first result gets a score of 1, the second 0.5, the third 0.33, etc. A higher MRR means the best answer is consistently at the top.
NDCG (Normalized Discounted Cumulative Gain): A complex but highly valuable metric that evaluates the ranking of multiple relevant documents. It penalizes the system if less relevant documents are placed above highly relevant ones.

We advise building a reference dataset (a so-called 'Golden Dataset') of 100 to 200 typical questions from your domain, linked to the documents containing the correct answer. Run your pipeline with and without a reranker on this dataset and compare the Recall and MRR. Only then can you be sure whether the extra latency outweighs the gain in accuracy.

Practical Step-by-Step Plan: How to Choose and Implement a Reranker

Do you want to get started with hybrid search and rerankers? Follow these steps to smoothly integrate it into your RAG application. If you don't have experience building a basic RAG system yet, we recommend first checking out the comprehensive guide on our learning module: RAG for beginners.

Step 1: Evaluate and optimize your current vector search. First, ensure your bi-encoder (embedding model) performs optimally. Use techniques like chunking and metadata filtering. A reranker cannot place documents at the top if your vector database doesn't retrieve them in the first place. Make sure your Recall@100 is high.
Step 2: Choose a Reranking model. There are various flavors. The Cohere Rerank API is currently the industry standard for closed models: powerful, multilingual, and easy to implement. If you are looking for an open-source alternative to run locally, look at the BGE-Reranker series (developed by BAAI) or the cross-encoders from Jina AI. These are excellent for self-hosting and avoid subscription costs.
Step 3: Determine the 'Top-K' balance. You need to configure how many documents your vector database sends to the reranker, and how many the reranker then passes to the LLM. A common "sweet spot" is retrieving the top 50 documents via vector search, passing them through the reranker, and then sending only the top 5 to the LLM to generate the final answer.
Step 4: Monitor and optimize. Once your system is in production, save the search queries and the documents chosen (or positively rated) by users. This allows you to expand your evaluation set and directly measure future adjustments to your reranking strategy.

Conclusion

Rerankers form an essential bridge between simple search algorithms and intelligent text generation. Where vector search excels in speed and scale, the reranker brings the necessary cognitive precision. Although adding a cross-encoder makes your architecture more complex and costs computation time, it is an indispensable step for almost any serious RAG application to prevent hallucinations and actually provide the user with the most relevant information.