RAG Reranking Skill

SkillAI & models

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

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the RAG Reranking Skill skill

What this skill tells your AI

The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/ai-agents-conversational/skills/rag-reranking/SKILL.md and read by ahel’s review.

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

Cross-Encoder Reranking: Sentence-transformer cross-encoders
Cohere Rerank: Cohere rerank-v3 API
MMR Reranking: Diversity-aware result filtering
LLM Reranking: Using LLM for relevance scoring
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)

Signals

GitHub stars: 2k
Forks: 112
Last commit: Sep 2026

Advanced

Item type: skill
Key: rag-reranking
Source: github.com/a5c-ai/babysitter

github.com/a5c-ai/babysitter