advanced-rag-optimizer

What problem does it solve?

Master Retrieval-Augmented Generation to build applications that provide accurate, grounded responses with verifiable source citations. This skill minimizes hallucinations by enabling agents to access and retrieve domain-specific knowledge effectively.

Core Features & Use Cases

• Chunking Strategy: Recursive Character Splitting, Semantic Chunking, and Markdown-Aware Splitting to preserve context and document structure.
• Retrieval Refinement: Hybrid Search, Query Transformation (Multi-Query generation, HyDE), and Contextual Compression for efficient retrieval and concise context.
• Precision & Reranking: Cross-Encoders and MMR to improve answer relevance and diversity.
• Evaluation (LLM-as-a-Judge): Faithfulness, Answer Relevance, and Context Precision to validate results against retrieved sources.

Quick Start

Configure and run the Advanced RAG Optimizer to build and deploy a production-grade RAG pipeline.