LLMs are trained on a large but fixed corpus of data, limiting their ability to reason about private or recent information. Fine-tuning is one way to mitigate this, but is often not well-suited for facutal recall and can be costly. Retrieval augmented generation (RAG) has emerged as a popular and powerful mechanism to expand an LLM's knowledge base, using documents retrieved from an external data source to ground the LLM generation via in-context learning. These notebooks accompany a video playlist that builds up an understanding of RAG from scratch, starting with the basics of indexing, retrieval, and generation.