Million-token context windows tempt you to skip RAG and paste everything in. For most enterprise workloads, that is the wrong call.
Stuffing the window has real costs:
- latency climbs as the window fills
- token cost scales with every query
- lost in the middle: accuracy drops in long contexts
RAG retrieves only the relevant passages, then grounds the answer in them. The model sees a focused slice, so responses stay fast, accurate, and citable whether you have 10 docs or 10,000.
The core point: context size is not accuracy. A model can read a million tokens and still surface the wrong fact.
CustomGPT.ai uses RAG so accuracy holds as the knowledge base scales, without paying to reprocess everything per query.
Big context is convenient for small tasks. Retrieval is what scales.
Full comparison: https://pollthepeople.app/is-rag-better-than-a-large-context-window/
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