# What Is a Reranker? The Second Stage That Lifts Retrieval Quality > Source: https://sukruyusufkaya.com/en/blog/reranker-nedir > Updated: 2026-07-05T16:06:30.971Z > Type: blog > Category: yapay-zeka **TLDR:** What is a reranker? A reranker is a model that re-orders the candidate documents retrieved in the first stage of a search or RAG system according to their true relevance to the query. This guide: a clear definition, why it is needed, how it works, the cross-encoder architecture, two-stage retrieval, its place in the RAG pipeline, retrieval quality, and FAQs. What is a reranker? A reranker is a second-stage model in a search or RAG system that takes the candidate documents returned by a fast first stage and re-orders them by their true relevance, scoring each candidate together with the query. This way the most relevant documents rise to the top of the list and the context sent to the model is cleaned up. A search system being fast does not mean it is accurate. The first stage uses a coarse method so it can run in milliseconds instead of seconds; the result is a relevant but roughly ordered candidate list. This is exactly the gap a reranker closes: it wins back the accuracy that speed gave up. This guide covers what a reranker is, why it is needed, how it works, its relationship to the cross-encoder, and its place in two-stage retrieval and the RAG pipeline. ## Why Is a Reranker Needed? The Retrieval Quality Problem The most common mistake in a search or RAG system is missing the difference between "retrieval works" and "the right document is at the top". The first-stage search is usually an embedding-based vector search: it is fast because it has vectorized all documents in advance and scans those semantically close to the query in one pass. But that closeness is coarse; the right document may be among the top 10 results, yet sit in 7th place. Here is the problem: the language model, or the user, usually looks only at the first few results. Even if the right document is in the list, if it is not near the top it is effectively lost. Retrieval quality is decided exactly here — what matters is not that the document is in the list, but that it is in the right position. A reranker takes the candidates the first stage found and re-orders them by true relevance, closing this accuracy gap and visibly improving retrieval quality. ## How Does a Reranker Work? A reranker handles the candidate list from the first stage one by one. It gives each candidate document to the model together with the user's question, and the model produces a numerical score for "how relevant is this document to this question?". All candidates are then re-ordered by this score and the top ones are selected. The critical distinction here is this: the first stage does the "find candidates" job, and the reranker does the "pick the best" job. The first stage is broad but loose; the reranker is narrow but sharp. Together they strike a balance neither could achieve alone: both speed and accuracy. ## The Difference Between Cross-Encoder and Bi-Encoder The key to understanding a reranker is telling apart two model approaches: the bi-encoder and the cross-encoder. First-stage search usually uses a bi-encoder: the query and document are each vectorized separately and independently, then the closeness of these two vectors is measured. This approach is fast because document vectors can be precomputed and stored. A reranker, on the other hand, is usually a cross-encoder: it gives the query and document to the model not separately but together as a single input. By seeing the mutual interaction of the words, the model makes a far finer relevance judgment. The cost of this is speed: because a cross-encoder runs a separate inference for each document, it cannot scale to scan the whole knowledge base. That is precisely why a cross-encoder is applied not to all documents but only to the narrow candidate list the first stage selected. This division of labor is the reason two-stage retrieval exists. ## What Are the Types and Variants of Rerankers? In practice there is no single kind of reranker; different needs suit different approaches. The most common and most accurate type is the cross-encoder reranker described above: it reads the query and document together and produces a deep relevance score. Its quality is high but its latency is also the greatest, which makes it ideal for short candidate lists. A second approach is LLM-based reranking: a large language model is instructed to "order these candidates from most to least relevant to this question". It is strong on flexible, reasoning-heavy queries, but its cost and latency can be even higher than a cross-encoder's. A third variant is score-fusion methods that combine several search signals (semantic score, keyword score, freshness, authority) into a single ranking; these raise retrieval quality by blending existing signals rather than running a separate model. Choosing the right type depends on the query's complexity, the latency budget, and the nature of the content; in most enterprise scenarios the cross-encoder reranker hits the practical balance point. You can explore the role of language models in such tasks in the what is an LLM guide and prompt design in the what is prompt engineering guide. ## Two-Stage Retrieval and Its Place in the RAG Pipeline Modern search and retrieval systems rest not on a single magic model but on a division of labor. The name of that division is two-stage retrieval: a broad, fast retrieval stage, followed by a narrow, precise reranking stage. The first stage works on "bring everything that might fit", the second on "surface the best". Inside a RAG pipeline the reranker sits exactly between retrieval and generation. Retrieval finds candidate documents, the reranker cleans these candidates and selects the most relevant, and only these select pieces are given to the language model as context. This way the model works with a genuinely relevant handful of documents rather than a noisy heap. We cover the whole of RAG and the role of the retrieval layer in the what is RAG guide; the reranker is the quality lever in that architecture. ## The Reranker's Effect on Retrieval Quality The concrete value of a reranker is moving the right document into the visible part of the list. The first stage usually retrieves the document containing the right answer but may place it 8th or 9th; a language model or user, in practice, takes only the first few results seriously. When the reranker moves this document to 1st or 2nd place, the answer the system gives improves fundamentally. This effect becomes especially clear on broad and heterogeneous enterprise content. In an organization's tens of thousands of pages of documentation, there are many pieces with similar keywords but different context; a bi-encoder struggles to tell them apart. A cross-encoder-based reranker, because it reads the query and document together, catches these fine distinctions. In short, retrieval quality often comes not from the model but from the presence of this second stage. To build an enterprise search-and-retrieval architecture end to end, see the enterprise RAG systems solution. ## Real-World and Türkiye Examples A reranker's value is not an abstract metric; it is felt directly in everyday products. When a search for "waterproof winter boots" is run on an e-commerce site, the first stage brings back hundreds of near products; the reranker evaluates the query's three attributes (season, waterproofing, product type) together and surfaces the best fits. In a customer support assistant, the reranker lifts the exact paragraph that solves the user's problem to the top out of thousands of help articles, and pushes down articles that look similar but are wrong. In the Türkiye context this second stage matters especially. Turkish, with its agglutinative structure and rich inflectional forms, strains keyword-only search; the phrases "fatura iadesi" and "faturamı iade" look different at the word level yet carry the same intent. Because a cross-encoder-based reranker captures this intent overlap through meaning rather than words, it markedly lifts retrieval quality on Turkish enterprise content. Open and commercial reranking models from providers such as OpenAI, Google, and Hugging Face have made this capability accessible for multilingual content, including Turkish. ## The Reranker's Limits and the Cost-Latency Trade-off A reranker is powerful but not free. Because it runs a separate model inference for each candidate, latency and compute cost rise linearly as the number of candidates grows. So setting up a reranker correctly is about answering the question "how many candidates will we re-order?" well. A reranker only chooses among the candidates it is given; if the right document is not in the pool, even perfect ordering cannot create it. So instead of trying to rescue a weak first stage with a strong reranker, you should first fix retrieval quality (chunking, embedding). The practical approach is clear: a reasonable candidate pool (typically the top 20-100 pieces) is taken from the first stage, the reranker orders them, and only the best few are passed to the model. Too many candidates blow up latency; too few raise the risk of leaving the right document out from the start. This balance is a deliberate engineering decision between the latency budget and the quality target. ## Frequently Asked Questions ### What is the difference between a reranker and embedding-based search? Embedding-based search converts the query and documents into vectors separately and compares their closeness; it is fast but coarse. A reranker reads the query and each document together and produces a single relevance score; it is slower but far more accurate. Together they form two-stage retrieval. ### Is a reranker mandatory for RAG? It is not mandatory but is often needed when retrieval quality is critical. The first stage returns a relevant but roughly ordered candidate list; the reranker cleans the context sent to the model by lifting the most relevant pieces. It can be skipped on small, consistent knowledge bases but makes a difference on broad enterprise content. ### Why is a cross-encoder reranker slow? Because a cross-encoder runs a full inference for each candidate document together with the query; it runs the model N times for N candidates. Embedding search scans in one pass since documents are vectorized in advance. That is why a reranker is applied only to the narrow list the first stage selects. ### How many candidates should be sent to the reranker? Typically the top 20 to 100 candidates from the first stage are sent to the reranker, and the best 3 to 10 of those are passed to the model. The exact number depends on the latency budget, content size, and query type; too many candidates raise latency, too few lose accuracy. ### Does a reranker reduce hallucination? It reduces it indirectly. When the reranker lifts the right document to the top of the context, the language model grounds its answer in a real source and is less likely to make things up. But a reranker is not a generation model; if the candidate pool is wrong, good ordering alone cannot guarantee a correct answer. ## In Short: What Is a Reranker? In short, the answer to what is a reranker is: a second-stage model that re-orders the candidate documents retrieved in the first stage by their true relevance to the query, surfacing the most relevant pieces. It is the precise leg of two-stage retrieval; it usually works as a cross-encoder and, inside the RAG pipeline, determines the quality of the context sent to the model, thereby improving retrieval quality. For the basics see the what is RAG, what is an LLM, and what is a token guides, and for an enterprise search-and-retrieval system start with AI consulting.