Why RAG exists

After this lesson you can: Say what problem retrieval solves that a bigger model does not.

First: Embeddings: meaning as coordinates

Watch

• What is Retrieval-Augmented Generation (RAG)? - IBM Technology, 7 min. Concise, highly relevant overview from a strong teaching channel, though language is unconfirmed.
• RAG Explained | All about RAG - Retrieval Augmented Generation - codebasics, 15 min. Clear short English explainer focused exactly on RAG for beginners who know embeddings already.
• Introduction To Undertsanding RAG(Retrieval-Augmented Generation) - Krish Naik, 21 min. A longer short-concept introduction likely gives enough context for engineers new to model workflows.

Notes

RAG exists because a language model’s built-in knowledge is fixed at training time, incomplete, and hard to inspect. Making the model bigger can improve general reasoning and recall, but it does not reliably add your company’s latest docs, today’s policies, private tickets, or the exact paragraph needed to answer a question. Retrieval solves the freshness and specificity problem by looking up relevant information outside the model at the moment the user asks.

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