ThinkWithPrompt by SynotticThinkWithPrompt

Create a recurring briefing on developments in my field

A scannable weekly or monthly briefing that ranks news, papers, and releases by relevance to your work and turns them into actions.

Research & SummariesFew-ShotStandardWritingFill-in Template

The prompt

Turn the items below into a briefing on the latest developments in my field. My field and focus: [e.g. "edge AI for robotics", "payments regulation in the EU"] My role and current projects: [what I'm working on, so relevance can be judged] Topics I track most closely: [3-6 priority areas] Items for this period: [PASTE headlines, abstracts, release notes, or articles with their source links and dates] Period covered: [e.g. week of ...] Length: [e.g. readable in 5 minutes] Build the briefing: 1. Top developments (up to 8) - for each: headline, source, a 2-3 sentence plain summary, relevance score 1-5 to my work with a one-line reason, and one concrete action or implication for me. 2. Deep dive (1-3 items) - the most important technical or strategic item explained in more depth: what changed, how it works, limitations, and what it would take to apply it. 3. Watch list - early signals worth tracking but not acting on yet. 4. Ignore list - items that look important but don't matter for my focus, with a short reason. 5. Questions to investigate before the next briefing. Rules: - Use only the items I provide; do not add news, papers, or numbers from memory. - Keep each item's source link with it. If a claim in an item is unverified or comes from a press release, say so. - Separate what the source says from your interpretation.

Highlighted [brackets] are blanks to replace with your own details.

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What to fill in

  • e.g. "edge AI for robotics", "payments regulation in the EU"
  • what I'm working on, so relevance can be judged
  • e.g. week of ...
  • e.g. readable in 5 minutes

How this prompt is built

This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.

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