RAG Implementation

End-to-end patterns for building Retrieval-Augmented Generation systems, from document ingestion through retrieval optimization to production deployment. Covers chunking strategies, embedding selection, vector store configuration, retrieval techniques, and evaluation frameworks.

When to Use This Skill

Use this skill for:

• Building RAG pipelines from scratch
• Choosing and configuring vector databases
• Designing document chunking strategies
• Selecting and fine-tuning embedding models
• Implementing hybrid retrieval (keyword + semantic)
• Optimizing retrieval quality with reranking
• Evaluating RAG system performance (faithfulness, relevance, recall)
• Scaling RAG systems for production (caching, batching, cost control)

Trigger phrases: "RAG pipeline", "retrieval augmented generation", "vector database", "document chunking", "embedding", "semantic search", "retrieval quality", "vector store", "document ingestion", "reranking", "hybrid search"