Retrieval Augmented Generation (RAG): From Theory to Production

Retrieval Augmented Generation (RAG) is a practical approach to building AI systems that are grounded, current, and domain-aware. This course is designed for developers and AI enthusiasts who want to master end-to-end RAG systems—from fundamentals and architecture to evaluation and production deployment. If you are transitioning into AI engineering, preparing for AI roles, or looking to enhance LLM capabilities with external knowledge sources, this course gives you the theory, the tooling, and the hands-on patterns you need to succeed.

Why RAG matters: large language models are powerful, yet they hallucinate, struggle with domain-specific facts, and operate with a knowledge cutoff. RAG tackles these problems by combining two components: a retriever that fetches relevant information from your knowledge base and a generator (LLM) that uses this retrieved context to produce accurate, grounded answers. You will learn how to design, implement, and optimize both sides of this pipeline.

What you will learn: RAG fundamentals and architecture; information retrieval techniques (keyword, semantic, hybrid); embeddings and vector databases; chunking and document preprocessing; LLM integration and prompt engineering for context-aware responses; evaluation methodologies and metrics; and production deployment with monitoring, security, and cost controls. Advanced topics include agentic RAG (query planning and tool use), multimodal RAG (text + images), graph-augmented retrieval, reranking, query rewriting, and temporal awareness.

Real-world applications explored in the course include enterprise knowledge assistants, customer support copilots, document Q&A, content analysis, analytics copilots over logs and data, compliance assistants with grounded citations, and domain-specific chatbots for healthcare, finance, and legal. Across these cases, you will build intuition for trade-offs—precision vs. recall, latency vs. accuracy, and cost vs. scale—and learn patterns that generalize across tools and vendors.

Skills you will gain: the ability to design RAG architectures; evaluate and choose embedding models; implement semantic search, BM25, and hybrid retrieval; create robust chunking pipelines with metadata; integrate LLMs via APIs or open-source runtimes; engineer prompts for grounded responses; measure both retrieval and generation quality; deploy and scale vector databases; add logging, monitoring, and A/B testing; and harden systems with security and privacy best practices.

Hands-on experience: throughout the course, you will stand up a vector database, ingest diverse document types, experiment with ANN indexes and reranking, build a baseline RAG pipeline, tune prompts and retrievers, and evaluate with frameworks like Ragas and LangSmith. You will iterate on chunking strategies, implement caching for latency reduction, and test different LLMs and embedding choices. By the end, you will have a production-ready blueprint with observability, cost controls, and reliability measures.

Career impact: RAG skills are directly applicable to AI engineering, platform teams, MLops roles, and product engineering. You will be able to design and ship features like knowledge-grounded chat, internal copilots, and intelligent search experiences—quickly and safely—while communicating trade-offs to stakeholders.

Prerequisites: basic Python experience, familiarity with REST APIs, and comfort reading technical documentation. No prior deep learning background is required—core concepts (tokens, embeddings, vector search) are explained from the ground up, then progressively deepened with practical examples and production considerations.