A comprehensive walkthrough of building an end-to-end Retrieval-Augmented Generation (RAG) pipeline. Covers all major stages: document ingestion, text chunking strategies (200–500 tokens with overlap), embedding generation using models like all-MiniLM-L6-v2, vector database storage and similarity search, and LLM-based response
Table of contents
Key TakeawaysUnderstanding the RAG System ArchitectureData Ingestion in a RAG PipelineText Chunking: Preparing Documents for RetrievalEmbedding GenerationVector EmbeddingStoring Vectors in a DatabaseRetrieval in a RAG PipelineGeneration with a Large Language ModelCode Demo: Building a Simple End-to-End RAG PipelineEvaluating RAG System PerformanceScaling and Production ConsiderationsCost and Latency OptimizationRAG vs Fine-TuningFAQConclusionResourcesSort: