3.3 KiB
Prompt: Quick Capture Templates
Job: Five copy-paste sentence starters optimized for clean metadata extraction. Each one is designed to trigger the right classification in your AMCS's processing pipeline.
When to use: Keep these handy as a reference. After a week of capturing, you won't need them — you'll develop your own natural patterns. But they're useful for building the habit early.
What you'll get: Five starter patterns with explanations of why each one works.
Why does formatting matter? Your AMCS's edge function uses an LLM to extract metadata from each capture — people, topics, action items, type. These templates are structured to give that LLM clear signals, which means better tagging, better search, better retrieval.
Output feeds into: N/A — reference tool.
No prompt block below. Unlike the other four, this isn't something you paste into AI. These are templates for what you type into your capture channel or say directly to any MCP-connected AI using "save this" or "remember this."
1. Decision Capture
Decision: [what was decided]. Context: [why]. Owner: [who].
Example: Decision: Moving the launch to March 15. Context: QA found three blockers in the payment flow. Owner: Rachel.
Why it works: "Decision" triggers the task type. Naming an owner triggers people extraction. The context gives the embedding meaningful content to match against later.
2. Person Note
[Name] — [what happened or what you learned about them].
Example: Marcus — mentioned he's overwhelmed since the reorg. Wants to move to the platform team. His wife just had a baby.
Why it works: Leading with a name triggers person_note classification and people extraction. Everything after the dash becomes searchable context about that person.
3. Insight Capture
Insight: [the thing you realized]. Triggered by: [what made you think of it].
Example: Insight: Our onboarding flow assumes users already understand permissions. Triggered by: watching a new hire struggle for 20 minutes with role setup.
Why it works: "Insight" triggers idea type. Including the trigger gives the embedding richer semantic content and helps you remember the original context months later.
4. Meeting Debrief
Meeting with [who] about [topic]. Key points: [the important stuff]. Action items: [what happens next].
Example: Meeting with design team about the dashboard redesign. Key points: they want to cut three panels, keep the revenue chart, add a trend line. Action items: I send them the API spec by Thursday, they send revised mocks by Monday.
Why it works: Hits multiple extraction targets at once — people, topics, action items, dates. Dense captures like this are the highest-value entries in your brain.
5. The AI Save
Saving from [AI tool]: [the key takeaway or output worth keeping].
Example: Saving from Claude: Framework for evaluating vendor proposals — score on integration effort (40%), maintenance burden (30%), and switching cost (30%). Weight integration highest because that's where every past vendor has surprised us.
Why it works: "Saving from [tool]" creates a natural reference classification. The content itself becomes searchable across every AI you use. This is how you stop losing good AI output to chat history graveyards.