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.
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.
Fill in the blanks and use this promptMore Research & Summaries prompts
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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