Meeting Notes to Action Items
Turning messy notes into a shareable recap
The prompt
Convert these raw meeting notes into: (1) a 3-sentence summary, (2) a bulleted list of decisions made, (3) a bulleted list of action items with owner names if mentioned. If no owner is mentioned, write "Unassigned." Notes: [PASTE NOTES]
What to replace
Swap these placeholders for your own details before running the prompt:
[PASTE NOTES]your own value
How to use this prompt
- Copy the prompt using the button above.
- Replace [PASTE NOTES] with your own details — the more specific you are, the better the output.
- Paste it into Claude and run it.
- If the answer feels generic, add constraints: audience, length, tone, and what to avoid. That single change fixes most weak output.
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ClaudeThe 5-Part Prompt Framework — Claude
Every strong prompt combines five parts: Role (who the AI should act as), Context (background info it needs), Task (the specific ask), Format (how you want the output structured), and Constraints (length, tone, things to avoid).
ClaudeChain-of-Thought Prompting — Claude
Asking a model to "think step by step" before answering measurably improves accuracy on reasoning, math, and multi-step logic tasks because it forces the model to externalize intermediate steps instead of jumping to a guess..
ClaudeRole + Constraints to Kill Vagueness — Claude
When answers are generic, the fix is almost always more specificity, not a longer prompt.
Topics
Related across the site
Build a Meeting Notes System
Turn rambling notes into decisions, owners and deadlines — in a format consistent enough that you can search it six months later. The value is in the structure, not the summary.
Build a Personal Study Assistant
Turn a stack of lecture notes into a study partner that quizzes you, explains what you got wrong, and tracks which topics you keep failing. You will finish with a reusable system prompt you can point at any subject.
Build Your Own Prompt Library
Stop rewriting the same prompt. Build a small, organised, tested personal library — and learn the versioning habit that stops it rotting into a folder of near-duplicates.
Open-weights model
An open-weights model has publicly downloadable parameters, letting anyone run it on their own hardware. Llama, Mistral, Qwen and DeepSeek are prominent examples.
Reasoning model
A reasoning model is trained to generate extended internal deliberation before answering. It trades latency and cost for substantially better performance on maths, logic, coding and multi-step problems.
Zero-shot prompting
Zero-shot prompting means asking a model to do a task with no worked examples — just an instruction. It works well for common tasks the model has seen extensively in training.