AI Productivity
Summarizing and Extracting What Matters
Turn repetitive work hours into minutes — with hands-on skills and ready prompts you try live
We drown daily in long text: reports, chats, articles, meeting minutes. Smart summarizing turns ten pages into a one-minute read — but the difference between a useful summary and a worthless one is what you asked to be extracted.
Core principle
Do not just say "summarize this". Define the shape and purpose: do you want the key points? the decisions? the tasks and owners? the numbers? Each purpose needs a different summary.Summarizing is not extraction
Summarizing gives you a miniature of the whole text. Extraction pulls out specific elements only (all numbers, all tasks, all objections). Know which one you want before you ask.Ready prompt — meeting notes into decisions and tasks
Act as an executive assistant. I will paste unstructured meeting text, and you reorganize it into three sections: (1) a three-line summary, (2) the decisions made, (3) a task table: Task | Owner | Due date. Ignore small talk. Text: [paste the meeting notes here].
Common mistake
Blindly trusting the numbers in a summary. The tool may misplace a figure or mix two. When a decision rides on a number, go back to the source and verify.Pro tip
For very long text, ask for a "two-level summary" — one line per section, then a general summary on top. You get a map to navigate quickly.Check Your Understanding (2 questions)
Question 1
What determines the quality of a summary more than anything?
💡 Why: A useful summary starts from defining what to extract and in what shape (points/decisions/numbers), not from text length.
Question 2
When do you need "extraction" rather than "summarizing"?
💡 Why: Extraction pulls specific elements (numbers/tasks/objections), while summarizing gives a miniature of the whole text.
The recommended next step unlocks only after the correct answer, and your progress is saved on this device.