RAG Pipeline Builder

The RAG Pipeline Builder skill guides you through designing and implementing Retrieval-Augmented Generation systems that enhance LLM responses with relevant context from your own data. RAG combines the power of large language models with the precision of information retrieval, reducing hallucinations and enabling AI to work with private, current, or domain-specific knowledge.

This skill covers the complete RAG stack: document ingestion, chunking strategies, embedding generation, vector storage, retrieval optimization, context injection, and response generation. It helps you make informed decisions at each stage based on your specific requirements for accuracy, latency, cost, and scale.

Whether you are building a documentation Q&A bot, a customer support system, or an enterprise knowledge assistant, this skill ensures your RAG implementation follows production best practices.

Core Workflows

Workflow 1: Design RAG Architecture

1. Define requirements:
   • Data sources and formats
   • Query types and patterns
   • Accuracy requirements
   • Latency budget
   • Scale expectations
2. Choose components:
   • Document loaders