What does the RAG Architect skill help with?

It helps developers design and optimize Retrieval-Augmented Generation systems, covering everything from vector database selection and chunking strategies to retrieval evaluation and reranking.

Does it support evaluation frameworks?

Yes, it provides guidance on using RAG evaluation frameworks like RAGAS and TruLens, focusing on metrics such as precision, recall, and NDCG to benchmark system performance.

How does this skill improve RAG accuracy?

It implements industry best practices such as hybrid search (combining vector and keyword search), metadata filtering, and reranking stages to ensure the most relevant context is provided to the LLM.

Which vector databases does this skill support?

It provides specific architectural guidance for popular vector stores including Pinecone, Weaviate, Chroma, Qdrant, Milvus, and pgvector.

RAG System Architect

Name: RAG System Architect
Author: Jeffallan

byJeffallan

•

データサイエンスとML

Architects production-grade Retrieval-Augmented Generation (RAG) systems and vector search infrastructures for knowledge-grounded AI applications.

The RAG System Architect skill provides expert-level guidance for building, optimizing, and evaluating Retrieval-Augmented Generation systems. It assists developers in selecting the right vector databases, designing efficient chunking strategies, and implementing advanced retrieval techniques like hybrid search and reranking. Whether you are building a document-based chatbot or a complex enterprise knowledge base, this skill ensures your AI remains grounded in factual data while maintaining high performance, scalability, and retrieval accuracy using industry-standard frameworks and metrics.

主な機能

01Top-k reranking and query transformation techniques

02Expert vector database selection and schema design (Pinecone, pgvector, Weaviate)

0315 GitHub stars

04Comprehensive RAG evaluation using metrics like Precision@K and MRR

05Advanced document chunking and metadata enrichment strategies

06Implementation of hybrid search and semantic retrieval optimization

ユースケース

01Optimizing semantic search performance for large-scale enterprise knowledge bases

02Designing idempotent data ingestion pipelines for multi-tenant AI applications

03Building a context-aware customer support chatbot grounded in internal documentation

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jeffallan/claude-skills rag-architect

For use in Claude.ai and ChatGPT

Download Skill