My document is bigger than the model context window, now what?

Context windows are larger than before but still finite. Stuffing a 200-page report into one prompt sometimes works but yields slow and unreliable answers. Three patterns fix this: chunk plus retrieval, hierarchical summarisation, or a long-context model for specific tasks.

Try this first

1. 1First decide the task: are you searching for a fact (RAG fits) or synthesising over the whole document (summarisation fits)? The right pattern depends on the goal.
2. 2For search: chunk and embed the document like RAG, and pull only relevant pieces. You never search a 200-page report by pasting everything.
3. 3For synthesis: use hierarchical summarisation. Generate a summary per chapter, then a summary of summaries. The chain costs more tokens but gives coherent results.
4. 4For specific tasks (legal lookup, contract comparison) long-context models (Claude Sonnet, Gemini Pro with large window) are an option. Mind the cost: one 500K-token prompt can be pricier than a whole day of normal chat.
5. 5Measure 'lost in the middle': long contexts often forget the middle. Test by placing known facts at different positions and seeing what the model recalls.

None of the above fits?

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