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.
Works with: Claude Code, Cursor, Codex CLI
Category: AI & Agents — see all ranked ›
- tashan score: 43.0
- Adoption: 2 repos
- Health: active
- GitHub stars: 10,728
- Contributors: 17
- License: MIT
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source ↗ · skill:Jeffallan/rag-architect
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