Retrieval-Augmented Generation (RAG) has become the backbone of how modern LLM applications access current, proprietary, and domain-specific knowledge — but most engineers learn it through scattered tutorials that stop at a basic demo. This course goes far deeper.

Through 600 scenario-based practice questions across six comprehensive tests, you'll build a working, production-level understanding of what it actually takes to design, build, evaluate, and operate a real RAG system.

You'll cover:

RAG Fundamentals & Core Concepts — what RAG is, why it exists, hallucination reduction, RAG vs. fine-tuning vs. prompt engineering
Embeddings, Vector Search & Similarity — embedding models, vector databases, cosine similarity, ANN search (HNSW), sparse vs. dense retrieval
Retrieval Strategies & Chunking — chunking strategies, hybrid search, re-ranking, query transformation, parent-child retrieval
RAG Architecture & System Design — indexing pipelines, multi-tenant security, API design, latency and cost optimization, scalability
Evaluation, Optimization & Debugging — faithfulness and relevance metrics, A/B testing, failure diagnosis, continuous production monitoring
Advanced Techniques & Production Deployment — agentic RAG, GraphRAG, multi-modal RAG, long-context tradeoffs, deployment patterns like canary and shadow testing

Every question comes with a full explanation, so you understand the reasoning behind each answer — not just the correct choice. Whether you're adding RAG to an existing LLM application, architecting a new system from scratch, or preparing for technical interviews in this space, this course gives you the depth to build RAG systems that actually work well in production.