RAG Architect - POWERFUL

Overview

The RAG (Retrieval-Augmented Generation) Architect skill provides comprehensive tools and knowledge for designing, implementing, and optimizing production-grade RAG pipelines. This skill covers the entire RAG ecosystem from document chunking strategies to evaluation frameworks, enabling you to build scalable, efficient, and accurate retrieval systems.

Core Competencies

1. Document Processing & Chunking Strategies

Fixed-Size Chunking

• Character-based chunking: Simple splitting by character count (e.g., 512, 1024, 2048 chars)
• Token-based chunking: Splitting by token count to respect model limits
• Overlap strategies: 10-20% overlap to maintain context continuity
• Pros: Predictable chunk sizes, simple implementation, consistent processing time
• Cons: May break semantic units, context boundaries ignored
• Best for: Uniform documents, when consistent chunk sizes are critical

Sentence-Based Chunking