rag-optimizer

What problem does it solve?

Streamlines complex Retrieval-Augmented Generation (RAG) pipelines by optimizing chunking, embeddings, indexing, scoring (including Reciprocal Rank Fusion), and reranking, reducing latency while improving recall and answer fidelity.

Core Features & Use Cases

• Chunking optimization: Strategic segmentation of documents for dense and sparse retrieval.
• Hybrid search orchestration: Dense + sparse search with RRF for robust ranking.
• RAGAS evaluation: Metrics and feedback loops for continual improvement.
• Agentic routing: Dynamic query routing across multiple sub-skills and data sources.
• Report generation: Automated improvement reports and handoff to tech-writer.
• Use Case: Tune a legal corpus for precise case-law retrieval with high recall and relevance.

Quick Start

Use this Skill to optimize a RAG pipeline by configuring chunking strategy, embedding models, and hybrid search with RRF reranking.