Foundations

Core concepts, vocabulary, and mental models for AI engineering. By the end of this section you will understand how large language models work, why RAG exists, and be comfortable with the basic building blocks of the LangChain framework.

Topics

• Introduction to AI & Generative AI — What AI is, how generative models differ from traditional ML, LLM capabilities and limitations, and why RAG is needed

• RAG Theory — The academic origins of Retrieval Augmented Generation, Dense Passage Retriever, and the evolution from fine-tuning to in-context learning

• LangChain Basics — First hands-on code: Documents, Loaders, Embeddings, Vector Stores, and Retrievers

• Modern RAG Architecture — The three-phase pipeline (Indexing, Retrieval, Generation) and how all components fit together

Prerequisites

None.

• Introduction to AI & Generative AI
• Introduction to RAG and Theoretical Foundations
• LangChain Framework and Core Components
• Modern RAG Architecture
• Practice — AI Foundations
• Assessment — Foundations