# RAG Reranking

> Cross-encoder reranking and MMR diversity filtering for improved retrieval quality

- Skill: `a5c-ai/rag-reranking` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add a5c-ai/rag-reranking`
- Raw SKILL.md: https://api.skillmd.com/api/skills/a5c-ai/rag-reranking/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: a5c-ai (https://skillmd.com/u/a5c-ai)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/a5c-ai/rag-reranking

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# RAG Reranking Skill

## Capabilities

- Implement cross-encoder reranking models
- Configure Maximal Marginal Relevance (MMR) filtering
- Set up Cohere Rerank integration
- Design multi-stage retrieval pipelines
- Implement diversity-aware reranking
- Configure score normalization and thresholds

## Target Processes

- advanced-rag-patterns
- rag-pipeline-implementation

## Implementation Details

### Reranking Methods

1. **Cross-Encoder Reranking**: Sentence-transformer cross-encoders
2. **Cohere Rerank**: Cohere rerank-v3 API
3. **MMR Reranking**: Diversity-aware result filtering
4. **LLM Reranking**: Using LLM for relevance scoring
5. **Reciprocal Rank Fusion**: Combining multiple retrievers

### Configuration Options

- Reranking model selection
- Top-k after reranking
- MMR lambda (relevance vs diversity)
- Score threshold filtering
- Batch size for reranking

### Best Practices

- Use cross-encoders for quality
- Balance relevance and diversity
- Set appropriate thresholds
- Monitor reranking latency

### Dependencies

- sentence-transformers
- cohere (optional)