Software Development

RAG Architect

Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality. Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, context augmentation, similarity search, or embedding-based indexing.

Publisher / source

Jeffallan/claude-skills (found on skills.sh, an awesome list, the CSV list)

Upstream record

Jeffallan/claude-skills

Version

Version 2

Published

2026-11-02

Publication review

Approved for publication

Content fingerprint

36e21290e5df36ed3eaf007d0e91781c7453dc46c6dedd6d31c1c382b28cf137