RAG Expert

What problem does it solve?

This Skill equips teams to design and deploy production-grade Retrieval-Augmented Generation (RAG) systems that ground LLMs in enterprise knowledge bases.

Core Features & Use Cases

• Structured knowledge grounding: combines offline indexing, embedding storage, and vector search to provide sources for generated answers.
• Chunking and indexing strategies: supports fixed-size, sentence-based, semantic, and document-structure chunking to preserve context.
• Embeddings and reranking: includes embedding selection, query transformation, and cross-encoder reranking for improved accuracy.
• Use Case: Build a knowledge-base assistant that answers questions with citations from internal docs, policy PDFs, and runbooks.

Quick Start

Begin by outlining the RAG pipeline you want to deploy: index your knowledge base, choose an embedding model, and connect a vector store to an LLM.