When deciding between Retrieval-Augmented Generation (RAG) and long context models, use three key criteria: (1) corpus size under 32K tokens favors long context, while larger corpora require RAG due to attention sink starvation and lost-in-the-middle effects; (2) query shape determines strategy—needle lookups work with RAG, but summarization queries need long context; (3) cost considerations favor RAG for large contexts since long context incurs full prefill costs on every call.
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Deep Dive
RAG vs long context: the 32K rule I use to decideAdded:
Every long context guide says just retrieve. Past 32,000 tokens, that advice flips. And nobody hands you the line for when to stuff it all in instead. So, I run three checks. One, does the corpus fit under 32,000 tokens?
Past that, it still loads, but it skims the middle and drops things.
Two is query shape. Take a 200,000 token policy dump, ask for the refund window.
Stuffed. That line sits at token 140,000, [music] where attention is thinnest. Retrieval pulls the 800 token refund chunk to the top, and the model nails it. But ask it to summarize every policy change this year, [music] and retrieval is cooked. No single chunk holds that answer, so it wants the whole thing in context.
Three is cost, since long context repays the full prefill on every call.
>> [music] >> Sure, retrieval grabs the wrong chunk, and long context forgets its middle.
Pick your failure. Size, shape, cost.
The line is 32,000.
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