Building and Evaluating Your First RAG Pipeline

Large Language Models are powerful, but they often lack access to your specific, private data. Building a Retrieval-Augmented Generation (RAG) pipeline is the industry-standard way to bridge this gap, allowing AI to answer questions using your own documents. This text-based course guides you through the entire lifecycle of a foundational RAG pipeline. You will transition from understanding core concepts to reading and analyzing practical implementations, and finally learning how to quantitatively evaluate both retrieval accuracy and generation quality. What you'll learn: Understand the foundational architecture of Retrieval-Augmented Generation (RAG) systems; Apply text chunking strategies and generate vector embeddings for semantic search; Configure simple vector storage solutions to index and retrieve relevant document segments; Evaluate retrieval performance using key metrics like precision and recall; Assess generation quality to ensure LLM responses are accurate, relevant, and free of hallucinations; Explore modern optimization patterns including hybrid search and basic metadata filtering. The course starts with essential terminology and the conceptual mechanics of vector space. You will then progress through step-by-step written walkthroughs of chunking, embedding, vector database querying, and robust evaluation methodologies. This program is designed for developers and technical enthusiasts new to AI engineering, requiring no prior experience with vector databases or LLM orchestration. Start reading today to unlock the power of custom data retrieval for AI applications.