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

•

Data Science & ML

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

About

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.

Key Features

Top-k reranking and query transformation techniques
Expert vector database selection and schema design (Pinecone, pgvector, Weaviate)
15 GitHub stars
Comprehensive RAG evaluation using metrics like Precision@K and MRR
Advanced document chunking and metadata enrichment strategies
Implementation of hybrid search and semantic retrieval optimization

Use Cases

Optimizing semantic search performance for large-scale enterprise knowledge bases
Designing idempotent data ingestion pipelines for multi-tenant AI applications
Building a context-aware customer support chatbot grounded in internal documentation

About

Key Features

Top-k reranking and query transformation techniques
Expert vector database selection and schema design (Pinecone, pgvector, Weaviate)
15 GitHub stars
Comprehensive RAG evaluation using metrics like Precision@K and MRR
Advanced document chunking and metadata enrichment strategies
Implementation of hybrid search and semantic retrieval optimization

Use Cases

Optimizing semantic search performance for large-scale enterprise knowledge bases
Designing idempotent data ingestion pipelines for multi-tenant AI applications
Building a context-aware customer support chatbot grounded in internal documentation