What is Re-ranking Models?
Introduction to Re-ranking
In a standard Retrieval-Augmented Generation (RAG) system, the search process is a tradeoff between speed and accuracy.
When a user asks a question, the Vector Database must search through millions of documents. To keep latency low, the database uses Bi-Encoders (standard Embedding models). The user’s query is converted to a vector, the documents are converted to vectors, and their similarity is calculated (Cosine Similarity).
While fast, this process is “shallow”: the query and document are compared only through their vectors. It often returns documents that are highly related to the topic, but don’t actually contain the answer to the specific question.
If you feed those weaker results to an LLM, it is more likely to give a wrong or hallucinated answer.
Re-ranking is a second step added to many RAG pipelines to address this. It is a more accurate but more computationally expensive scoring pass that takes the top results (for example, the top 50) from the fast Vector Search and re-orders them by relevance to the query.
How Re-ranking Works: The Cross-Encoder
Re-ranking typically uses a different model architecture called a Cross-Encoder.
The Bi-Encoder Limitation (The Fast Search)
In standard vector search, the question and the document are encoded separately. The model never sees them together. It turns each into a vector and measures the distance between them.
The Cross-Encoder Intelligence (The Slow Re-rank)
A Cross-Encoder model (like those provided by Cohere or BAAI) processes the Question and the Document at the exact same time.
It concatenates them: [Question] + [Document].
It feeds this combined text into its Transformer Attention Mechanism. Because the model can see both texts simultaneously, its Attention layers model the relationships between the words in the question and the words in the document.
The Cross-Encoder acts somewhat like a human judge. It reads the pair and outputs a relevance score that estimates: “Does this document explicitly answer the user’s question?”
The Pipeline Architecture
Because Cross-Encoders must run once per query and document pair, running them across a database of a million documents for every query is impractical. Therefore, Re-ranking is usually implemented as a two-stage pipeline.
- Stage 1: Retrieval (Broad & Fast). The Vector Database uses fast, cheap Bi-Encoders (or BM25 Hybrid Search) to search a large collection and retrieve the Top 50 most likely candidates quickly.
- Stage 2: Re-ranking (Narrow & Slow). The system takes those 50 documents and passes them to the Cross-Encoder. The Cross-Encoder scores each of the 50 documents against the user’s query. This is slower per document than vector search, but manageable for a short candidate list.
- Stage 3: Generation. The system takes the Top 5 newly re-ranked documents (discarding the other 45) and feeds them into the LLM prompt.
The Business Impact
Implementing a Re-ranking model (often via a simple API call to Cohere’s Rerank endpoint) is often one of the simpler, higher-value improvements a developer can make to a RAG system.
It usually takes little code, and it can improve retrieval quality noticeably; the gain depends on the data and should be measured on your own evaluation set. By giving the LLM more relevant passages, Re-ranking reduces the chance of hallucinated answers, which matters for customer-facing applications.
Conclusion
Re-ranking models act as a quality-control layer in modern information retrieval. By separating the search process into a fast, scalable retrieval phase and a slower, more precise scoring phase, engineers can work around the limits of vector similarity alone. The Cross-Encoder makes it more likely that the final context given to the Generative AI is precise and relevant, which supports a more trustworthy RAG application.
Learn More
To learn more about the Data Lakehouse, read The Lakehouse Built for Everyone by Alex Merced. You can find this and other books by Alex Merced at books.alexmerced.com.