ai-ml/rag-architect

What problem does it solve?

This Skill provides comprehensive guidance and example workflows for designing and implementing high-quality RAG systems, addressing the challenge of effective document retrieval and semantic search integration.

Core Features & Use Cases

• Semantic Search Optimization: Strategies for chunking, embedding, and hybrid search pipelines.
• Pipeline Automation: Detailed code examples for document chunking, embedding generation, and retrieval tuning.
• Evaluation & Monitoring: Frameworks for assessing retrieval quality, reranking, and ongoing performance metrics.
• Use Case: For teams building AI knowledge bases, this Skill streamlines the setup of multi-layer retrieval pipelines and continuous performance evaluation.

Quick Start

Load the provided code snippets to implement document chunking, embedding, and hybrid search, then integrate evaluation tools to monitor retrieval accuracy and coherence.