Concept Intermediate · 3 min read

What is reranking in RAG

Q: What is reranking in RAG

In Retrieval-Augmented Generation (RAG), reranking is the process of reordering retrieved documents or passages based on their relevance to the query using a secondary AI model. This step improves the quality of context provided to the language model, leading to more accurate and grounded responses.

Quick answer

In Retrieval-Augmented Generation (RAG), reranking is the process of reordering retrieved documents or passages based on their relevance to the query using a secondary AI model. This step improves the quality of context provided to the language model, leading to more accurate and grounded responses.

Reranking is a post-retrieval process in Retrieval-Augmented Generation (RAG) that reorders retrieved documents to prioritize the most relevant information for better AI-generated answers.

How it works

Reranking in RAG acts like a quality filter after initial retrieval. First, a retrieval system (e.g., vector search) fetches a set of candidate documents related to the query. Then, a reranker model scores and reorders these candidates by estimating their relevance more precisely. This is similar to how a search engine might first find many results, then reorder them to show the best matches on top.

This improves the input context for the language model, ensuring it generates answers based on the most pertinent information rather than noisy or less relevant data.

Concrete example

Below is a simplified Python example using the OpenAI SDK to demonstrate reranking in a RAG pipeline. First, a vector search returns candidate documents. Then, a reranker model scores each candidate by prompting it to rate relevance. Finally, the documents are sorted by these scores before being passed to the language model for answer generation.

python

import os
from openai import OpenAI

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

# Example query
query = "What causes rainbows?"

# Step 1: Retrieve candidate documents (mocked here as a list)
candidates = [
    "Rainbows are caused by light refraction in water droplets.",
    "Rainbows appear when sunlight passes through raindrops.",
    "Rainbows are a type of optical illusion.",
    "Rainbows can be seen only after rain."
]

# Step 2: Rerank candidates by scoring relevance with a model
reranked = []
for doc in candidates:
    prompt = f"Rate the relevance of this document to the query '{query}' on a scale of 1 to 10:\nDocument: {doc}\nScore:" 
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": prompt}]
    )
    score_text = response.choices[0].message.content.strip()
    try:
        score = float(score_text)
    except ValueError:
        score = 0.0
    reranked.append((score, doc))

# Step 3: Sort documents by descending score
reranked.sort(key=lambda x: x[0], reverse=True)

# Step 4: Use top reranked documents as context for final answer generation
context = "\n".join(doc for _, doc in reranked[:2])
final_prompt = f"Using the following context, answer the question: {query}\nContext:\n{context}\nAnswer:"

final_response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": final_prompt}]
)

print("Final answer:", final_response.choices[0].message.content.strip())

output

Final answer: Rainbows are caused by sunlight refracting through water droplets in the air, creating a spectrum of colors visible after rain.

When to use it

Use reranking in RAG when your initial retrieval returns many candidates with varying relevance, and you want to improve answer accuracy by prioritizing the best context. It is essential when retrieval methods are approximate or noisy, such as vector similarity search on large corpora.

Do not use reranking if your retrieval system already returns highly precise results or if latency constraints prohibit additional model calls.

Key terms

Term	Definition
Retrieval-Augmented Generation (RAG)	An AI architecture combining retrieval of documents with language model generation to produce grounded answers.
Reranking	A post-retrieval step that reorders candidate documents by relevance using a secondary AI model.
Vector search	A retrieval method that finds documents based on vector similarity to a query embedding.
Context	The set of documents or passages provided to a language model to inform its response.

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Key Takeaways

Reranking improves RAG by prioritizing the most relevant retrieved documents before generation.
Use a secondary AI model to score and reorder candidates for better context quality.
Reranking is crucial when initial retrieval is approximate or noisy.
Avoid reranking if retrieval is already precise or if latency is a concern.

Verified 2026-04 · gpt-4o, gpt-4o-mini

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