Reranking
A second-pass model that reorders retrieved chunks by true relevance to the query.
/ quick answer
In RAG, vector search returns a candidate set that's fast but noisy. A reranker (usually a cross-encoder like Cohere Rerank or bge-reranker) scores each candidate against the query and reorders them, dramatically improving answer quality. A second-pass model that reorders retrieved chunks by true relevance to the query.
What is Reranking?
In RAG, vector search returns a candidate set that's fast but noisy. A reranker (usually a cross-encoder like Cohere Rerank or bge-reranker) scores each candidate against the query and reorders them, dramatically improving answer quality.
What is an example of Reranking?
Retrieve top 50 chunks by vector similarity → rerank down to top 5 → send only those to the LLM. Latency +200ms, accuracy often +20–40%.
Why does Reranking matter for AI and automation?
A second-pass model that reorders retrieved chunks by true relevance to the query. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →Chunking
Splitting documents into retrievable pieces before embedding them for RAG.
Related workflows
Turn this into a repeatable process.
- →Build an Internal Knowledge Bot
Ship a Slack bot that answers questions from your company docs.
- →RAG Content Ingestion Pipeline
Convert messy docs into searchable, cited knowledge chunks for AI systems.
- →Context Window Optimization Workflow
This workflow outlines steps to optimize the information fed into an LLM's finite context window, ensuring maximal relevance and efficiency while managing token limits.
- →How to Create a Website with AI
Go from idea to a live, custom-domain website in one afternoon using AI builders.
Related tool stacks
The tools that run it in production.
- →RAG Starter Stack
Minimum viable stack to ship a production RAG chatbot.
- →Knowledge Graph Stack
Relationship layer that maps concepts, workflows, prompts, tools, and cases.
- →AI Research & Knowledge Stack
Default toolset for analysts, founders and creators doing deep research with AI.
- →AI Support Agent Stack
Tier-1 support handled by an AI agent grounded on your docs, with human handoff.
Related prompts
Reusable prompts for this job.
- →RAG Answer With Strict Citations
Force the LLM to answer only from provided chunks and cite them by ID.
Comparisons & alternatives
Pick between the options.
- →RAG vs Long Context Windows
Is RAG obsolete now that context windows are 1M+ tokens? Not quite — here's when each wins.
- →ChatGPT vs Claude
Two leading conversational AI assistants compared across reasoning, writing, coding, and pricing.
- →Lovable vs Bolt
Two AI app builders compared on speed, backend, deployment, and production readiness.
- →OpenAI API vs Anthropic API
Choosing between the two leading LLM API providers for production apps.