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name: rag-reranking description: Cross-encoder reranking and MMR diversity filtering for improved retrieval quality allowed-tools:

• Read
• Write
• Edit
• Bash
• Glob
• Grep graph: domains: [domain:software-engineering] specializations: [specialization:ai-agents-conversational] skillAreas: [skill-area:retrieval-augmented-generation, skill-area:model-evaluation] roles: [role:ml-engineer, role:backend-engineer] workflows: [workflow:ml-model-lifecycle, workflow:feature-development]

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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)