In this one, I discuss the dilemma between using retrieval-based generation and the newer "long context models".

Long context models, like the Gemini suite of models, allow us to send up to millions of tokens (thousands of text pages), whereas retrieval (RAG)-based systems allow us to search through as much (if not more) content and retrieve only the necessary bits to send the LLM for improved answers.

Both have advantages and disadvantages. This short episode will help you better understand when to use each.

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