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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 oftennot well-suited for facutal recall andcan 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 avideo playlist that builds up an understanding of RAG from scratch, starting with the basics of indexing, retrieval, and generation.