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Generic summaries ('this document discusses...') are nearly useless. A good AI summary is an answer to a question: what changed from the last version, what are the obligations, what would this cost us. The technique is asking for the right structure, then verifying the claims that matter.
Tell the model the decision or question the summary serves. 'Summarize this lease' produces prose; 'Extract every obligation, deadline, and cost we take on under this lease' produces something you can act on.
Ask for a fixed shape — obligations table, key numbers, red flags — with a supporting quote for each substantive claim. Quotes make verification a ten-second lookup instead of a re-read.
Very long documents summarize badly in one pass. Summarize section by section, then synthesize the sections — you keep detail and the final synthesis has real inputs instead of a compressed blur.
Check the two or three summary points your decision rests on against the source pages. Summaries are reliable on themes and occasionally wrong on specifics — numbers, dates, negations. Verify specifics.
If you summarize the same kind of document weekly (contracts, reports, minutes), save your refined instruction as a reusable prompt or wire it into a workflow with a watch folder. The tuning you did is worth keeping.
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