RAG (Retrieval Augmented Generation) is over-built for small datasets that fit within the model's context window (under 100k tokens), where direct data injection is free, fast, and requires no infrastructure; RAG becomes necessary only when data changes frequently (daily, hourly, or every 12 hours) or when the product needs to cite resources and show data sources.
Deep Dive
Prerequisite Knowledge
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Where to go next
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Deep Dive
Everyone Is Using RAG WrongAdded:
Rag is the most over-built and under-thought technology right now. Let me ask you series of questions and help you re-evaluate on your decision of rag.
First question, is your data small and it fits under the model's context window? Or let's say your data is under 100k tokens, then you don't need rag.
Just take your data and insert it into model's context window. It's free, [music] it's fast, and it doesn't have any infrastructure cost. Second question, does your data change too frequently? Let's say your data changes every day, every hour, or every 12 hours, then you need rag. Third question, does your product needs to cite resources? Let's say you want to show your user where the data came from, then you need rag. I have made a detailed YouTube video on how rag works.
Comment rag and I'll send you the direct video link.
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