RAG (Retrieval-Augmented Generation) pipelines combine search engines with large language models to provide accurate, grounded responses by retrieving relevant information before generating answers. The guide covers building a complete RAG system from data ingestion and chunking through embedding generation, vector storage with Meilisearch, and integration with generative models. Key considerations include choosing appropriate tools, optimizing chunking strategies, monitoring performance, managing costs, and implementing security measures for production deployments.
Table of contents
What is a RAG pipelineArchitecting your RAG pipelineBuilding a RAG pipeline step-by-stepDeploying and optimizing your RAG pipelineAdvanced considerations for RAG pipelinesYour RAG Pipeline Journey Starts HereSort: