agentsop-reranker-stage

Rerank bi-encoder candidate sets with cross-encoders to improve RAG precision.

287|16|Updated May 20, 2026

Search Tags:#rag #evaluation #reranking #cross-encoder #bi-encoder #precision tuning #latency cost

Ready to run

npx skills add https://github.com/agentsope/SkillAlchemy --skill agentsop-reranker-stage

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agentsop-reranker-stage
Source: https://github.com/agentsope/SkillAlchemy/tree/main/skills/agentsop-reranker-stage
Command: npx skills add https://github.com/agentsope/SkillAlchemy --skill agentsop-reranker-stage

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

It fixes RAG cases where the correct document is retrieved but buried in the candidate list, causing low top-1 precision and weak MRR despite a reasonable hit-rate.

Core Features & Use Cases

Precision upgrade via wide→narrow reranking: retrieves a wide candidate pool with a bi-encoder (recall) and then reranks narrowly with a cross-encoder (precision).
Production-ready activation rules and boundaries: activates only when the bottleneck is precision (not recall), and defines when not to rerank to avoid wasted latency.
Cross-framework implementation guidance: maps the same reranking stage to LlamaIndex node postprocessors and other retrieval stack patterns.

Quick Start

Use the agentsop-reranker-stage skill to decide whether to add a reranker and how to tune the candidate width and rerank cutoffs (N=20-50, k=3-5) for your plateaued RAG pipeline.

Frequently Asked Questions about agentsop-reranker-stage

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I fix low top-1 precision in RAG when the gold document is retrieved but not ranked first?▼

To fix low top-1 precision in RAG, apply a wide bi-encoder retrieve and narrow cross-encoder rerank workflow. This reranking technique improves MRR by promoting the gold document from the candidate pool to the top position.

When should I add a cross-encoder reranker to my RAG pipeline?▼

Add a cross-encoder reranker to your RAG pipeline when the bottleneck is precision, not recall. Activation rules dictate reranking is needed when experiencing retrieval precision plateaus or misplaced relevant chunks despite reasonable hit-rates.

What are the recommended candidate width and rerank cutoffs for RAG reranking?▼

Recommended RAG reranking cutoffs involve retrieving a wide candidate pool of N=20-50 chunks using a bi-encoder, then applying a cross-encoder to rerank down to a narrow top-k of 3-5 for high-precision context delivery.

Does this reranking stage work with LlamaIndex node postprocessors?▼

Yes, this reranking stage maps to LlamaIndex node postprocessors and other retrieval stack patterns. It provides cross-framework implementation guidance to encode the bi-encoder retrieve and cross-encoder rerank workflow across different RAG frameworks.

What are the latency and cost trade-offs of using a cross-encoder for RAG reranking?▼

Cross-encoder RAG reranking introduces latency and computational cost due to narrow candidate scoring. To manage this, apply production-ready activation boundaries and evaluate latency and cost budgeting against MRR and faithfulness gating before deployment.

Why does my RAG pipeline have weak MRR despite a reasonable hit-rate?▼

Weak MRR despite a reasonable hit-rate occurs when the correct document is retrieved but buried in the candidate list. This RAG precision issue is resolved by implementing a narrow cross-encoder reranking stage over the wide bi-encoder candidate pool.