---
type: concept
title: "Reranking"
description: "Applying a second, more precise model to reorder an initial candidate set so the most relevant chunks land at the top of the context."
tags: [reranking, retrieval-quality, rag, relevance]
videos: [every-rag-strategy-explained-in-13-minutes-no-fluff, your-ultimate-n8n-rag-ai-agent-template-just-got-a-massive-upgrade, all-the-best-rag-strategies-in-one-mcp-for-ai-coding-assistants, the-easiest-possible-strategy-for-accurate-rag-step-by-step-guide, the-proven-solution-for-unbelievable-rag-performance-lightrag-guide, build-ai-agents-that-evolve-over-time]
created: 2026-07-21
updated: 2026-07-21
---

# Reranking

Reranking is a two-stage retrieval pattern: cast a wide net first, then use a specialized model to reorder the catch so only the truly relevant chunks reach the LLM. Cole describes the shape directly: "First, we're going to pull a large number of chunks from our vector database. But then we're going to use this specialized reranker model often a cross encoder to find the ones that are actually relevant" (every-rag-strategy, 0:02:43). The reranker's whole job is precision, "to take the chunks that we have returned from our vector database and order them into the ones that are actually the most relevant" (all-the-best-rag-strategies, 0:16:19).

## Why it matters

Handing an LLM everything the vector search returned backfires. "If we were to go to our LLM and just give it 20, 50 or more chunks right away, we're going to completely overwhelm it" (every-rag-strategy, 0:03:07), and "25 chunks leads to a very high risk of hallucinating" (n8n-massive-upgrade, 0:16:14). Reranking lets you retrieve generously for recall, then filter hard for precision: "you can return a lot of chunks from rag like 50 different chunks and then you use re-ranking and then only return the 10 most relevant" (all-the-best-rag-strategies, 0:18:20).

## How it works

A reranker is a different class of model from the generator. "You can think of it as a special kind of model. Not a large language model, but a re-ranker model where its sole job is to take in a massive amount of chunks and it's going to rerank and filter those out only returning the top four" (n8n-massive-upgrade, 0:15:41). The workhorse architecture is a cross encoder that scores the query and each chunk together: "It uses a separate kind of AI model, a model called a cross encoder to analyze the query and the chunks that we got back and then order them based on the evaluated relevance" (all-the-best-rag-strategies, 0:16:36). Crucially, rerankers are cheap and fast because they are narrow: "re-rankers are designed to handle this much information, and they're a lot cheaper and faster. So, they don't have general intelligence like a large language model" (n8n-massive-upgrade, 0:16:31).

## Builds on

- [Retrieval-Augmented Generation (RAG)](./rag.md) is the pipeline reranking refines; it operates on whatever the retrieval step returns.
- [Semantic Search](./semantic-search.md) is the typical first stage that over-fetches candidates for the reranker to reorder.
- [Hybrid Search](./hybrid-search.md) pairs naturally with reranking, feeding a merged candidate pool into the second-stage model.

## Related

- [Query Expansion](./query-expansion.md) improves what enters the pipeline; reranking improves what leaves it. Cole lists them side by side as strategies worth stacking (lightrag guide, 0:22:42).
- [Contextual Retrieval](./contextual-retrieval.md) is a complementary precision technique applied at ingestion rather than after retrieval.
- [Agentic RAG](./agentic-rag.md) and [Agentic Search](./agentic-search.md) can invoke reranking as one tool among many. In self-evolving memory, "they implement advanced rag techniques like reranking relevant scores, including all the metadata and timestamps" to keep long-term memory robust (build-ai-agents-that-evolve, 0:23:46).

## Sources

- [Every RAG Strategy Explained in 13 Minutes (No Fluff)](../sources/every-rag-strategy-explained-in-13-minutes-no-fluff.md) - "[0:02:43] First, we're going to pull a large number of chunks from our vector database. But then we're going to use this specialized reranker model often a cross encoder to find the ones that are actually relevant"
- [Your ULTIMATE n8n RAG AI Agent Template just got a Massive Upgrade](../sources/your-ultimate-n8n-rag-ai-agent-template-just-got-a-massive-upgrade.md) - "[0:16:31] re-rankers are designed to handle this much information, and they're a lot cheaper and faster. So, they don't have general intelligence like a large language model."
- [All the BEST RAG Strategies in ONE MCP for AI Coding Assistants](../sources/all-the-best-rag-strategies-in-one-mcp-for-ai-coding-assistants.md) - "[0:16:36] It uses a separate kind of AI model, a model called a cross encoder to analyze the query and the chunks that we got back and then order them based on the evaluated relevance"
- [Build AI Agents that EVOLVE Over Time](../sources/build-ai-agents-that-evolve-over-time.md) - "[0:23:46] they implement advanced rag techniques like reranking relevant scores, including all the metadata and timestamps, really making sure that that long-term memory is super robust"
- [The PROVEN Solution for Unbelievable RAG Performance (LightRAG Guide)](../sources/the-proven-solution-for-unbelievable-rag-performance-lightrag-guide.md) - "[0:22:42] You also have agentic rag, query expansion, reranking. There's so many strategies that I want to keep diving into on my channel."
- [The EASIEST Possible Strategy for Accurate RAG (Step by Step Guide)](../sources/the-easiest-possible-strategy-for-accurate-rag-step-by-step-guide.md) - "[0:22:44] They talked about re-ranking and things that I didn't want to dive into in this video because I wanted to just get you started with something very tangible"
