Welcome to RAG Fundamentals, RAG Systems, and Advanced RAG.

Large Language Models are incredibly powerful, but they have two major blind spots: they don’t know your private data, and they confidently hallucinate when they lack context. Retrieval-Augmented Generation (RAG) solves both problems by anchoring AI to your actual documents, creating systems that are accurate, verifiable, and secure.

Whether you are building your first AI prototype or scaling a complex enterprise application, this course takes you from the absolute basics to state-of-the-art architectures.

Here is what we will cover:

1. RAG Fundamentals

We start at the ground floor. You will learn the core mechanics of how RAG bridges the gap between raw data and LLMs.

• Embeddings & Vector Spaces: How text is translated into numbers.

• Chunking Strategies: Breaking down documents for optimal retrieval.

• Vector Databases: Storing and querying data efficiently.

2. RAG Systems

Next, we turn theory into architecture. You will learn how to connect the pieces into a functional, end-to-end pipeline.

• The Retrieval Pipeline: Connecting user queries to your vector store.

• Prompt Augmentation: Injecting context into the LLM safely and effectively.

• Frameworks: Getting hands-on with tools like LangChain and LlamaIndex to build production-ready systems.

3. Advanced RAG

Finally, we tackle the hard problems. You will learn how to optimize your system for accuracy, speed, and complex reasoning.

• Advanced Retrieval: Implementing hybrid search, re-ranking, and query transformations.

• Agentic RAG: Building systems that can plan, route queries to different tools, and self-correct.

• Evaluation & Optimization: Using frameworks like RAGAS to test your pipeline and eliminate hallucinations.