data-ai-ml-rag-architect

What problem does it solve?

This Skill helps design, evaluate, and optimize retrieval-augmented generation (RAG) architectures, covering vector stores, chunking pipelines, embeddings, and retrieval strategies to ground LLM outputs in knowledge.

Core Features & Use Cases

• Architect end-to-end RAG pipelines including vector stores, chunking, embedding models, and hybrid search.
• Evaluate multiple embeddings and retrieval configurations, tune latency and accuracy, and monitor metrics like precision@k, recall@k, and MRR.
• Design and enforce metadata, versioning, deduplication, and multi-tenant filtering for scalable knowledge-grounded AI apps.
• Use cases include enterprise document QA, knowledge bases, and debugging retrieval quality in data-intensive domains.

Quick Start

Frame a production-ready RAG workflow by selecting a vector store, designing a chunking strategy, enabling hybrid search, and validating retrieval quality.