RAG Architecture

What problem does it solve?

This Skill helps you design and optimize Retrieval-Augmented Generation (RAG) pipelines, ensuring efficient and accurate information retrieval for your AI applications.

Core Features & Use Cases

• End-to-End Design: Covers document analysis, chunking, embedding, vector database selection, and retrieval optimization.
• Customizable Strategies: Provides options for various chunking methods, embedding models, and vector stores.
• Use Case: You need to build a RAG system for your company's internal knowledge base. This Skill will guide you through selecting the best chunking strategy for your documents, choosing an appropriate embedding model, and deciding on a vector database that fits your performance and cost requirements.

Quick Start

Design a RAG pipeline for a corpus of technical documentation, focusing on semantic chunking and the Pinecone vector database.