Complete RAG Tutorial 2026: Build AI Apps with Retrieval Augmented Generation

A tutorial series on retrieval-augmented generation that connects LLMs to external knowledge bases. The description covers the path from RAG fundamentals and architecture through vector databases and embeddings to building applications.

Covered

• Vector databases and embeddings.
• Implementation with Python and LangChain.
• Production deployment and real-world application examples.

Who it suits

Python programmers who are learning LLMs and want to extend chat-style apps with their own data, or data scientists exploring search systems built on language models.

Reading the listing

The description leans on broad phrases such as real-world applications and production deployment rather than naming a fixed syllabus. Use the video list itself to confirm which parts of the stack, such as the vector store or the framework, each episode covers before you commit to the whole series.