Keep context through a fragmented workday
Give AI the context your real work depends on.
Turn scattered decisions, constraints, and sources into a brief AI can use—and you can verify. The book takes you from one ten-minute context note to a repeatable seven-day practice.
EPUB · 166 pages · templates + verification checklist + seven-day practice · applicable VAT is shown before payment.
Put the method to work
Get a useful result from the first session.
Each step uses work already on your desk and produces a visible artifact you can use immediately.
Build one context note from work already on your desk.
Capture the decision, owner, constraints, evidence, and next move.
Reuse that context to brief AI and verify what it produces.
From useful notes to accountable AI
Preserve the context AI was never there to see.
AI cannot infer why the decision changed, which standard matters, what the client rejected, or what your team already tried. A context note makes that hidden thread available for better work.
After meetings
Preserve the decision, owner, constraints, uncertainty, and exact next move.
Across interruptions
Leave enough context to return without reconstructing the previous hour of thought.
When using AI
Brief the tool with real situations and verify its output against your standards and sources.
Completed example
Give AI a brief it can be held accountable to.
This free preview shows one completed brief. The paid book gives you the reusable template, verification checklist, worked examples, and a seven-day practice for making the method stick.
“Summarize the pilot and recommend next steps.”
The model must guess the audience, budget, deadline, current decision, and which sources deserve trust.
The recommendation has a standard to meet.
Facts, uncertainties, and required checks are visible before the draft begins.
Completed Context Brief
- Outcome
- Recommend the pilot scope and next-week plan.
- Decision owner
- Operations director.
- Current decision
- Keep the pilot with Team A until support capacity is confirmed.
- Constraints
- €18k cap; client decision due Friday; Support needs two weeks.
- Sources
- Finance estimate v3; client email dated 4 September; Support capacity note.
- Open uncertainty
- Whether the revised onboarding cost includes training.
- AI task
- Draft a five-bullet decision note for leadership.
- Verify
- Numbers against Finance; dates against the client email; capacity with Support.
Resulting AI output
Recommendation: keep the pilot with Team A for the next cycle.
- The €18k cap and two-week support lead time make a wider launch premature.
- Confirm whether training is included in the revised onboarding cost before Friday.
- Ask Support to validate its earliest capacity date.
- Return to leadership with the confirmed cost, capacity date, and expansion trigger.
The draft used named constraints and separated a recommendation from an unresolved fact.
The training cost and Support date remain human verification tasks.
Copy the structure
The Context Brief
Outcome: Decision owner: Current decision: Constraints and standards: Sources and evidence: Open uncertainty: Next move: What AI should produce: How I will verify it:
Inside the book
A context habit that travels with your work.
Build the practice with tools you already use. Preserve what future action and judgment will require, then carry it across models, meetings, projects, and interruptions.
- Return to interrupted work without reconstructing the previous hour
- Turn meetings into decisions, owners, constraints, and next moves
- Brief AI with standards and sources instead of fragile prompt formulas
- Verify AI output against your own evidence
- Use the notes app you already have
Delivery and terms
Know exactly what arrives.
- File
- One 3.1 MB EPUB, available instantly after checkout
- Reading
- Read in your preferred EPUB-compatible app
- Included
- 166 pages, 17 sections, two bonus chapters, templates, and a seven-day reset
- Refunds
- 14-day refund policy shown at checkout
Why I write these books
The same problem followed me from one role to the next.
I began with physics and mathematical models in finance, then moved into software, projects, products, and technical communication. Across those roles, I kept coming back to the same challenge: deciding what mattered and preserving enough context to act while priorities changed.
Note-Taking for AI applies that lesson to the brief you give a model: preserve the decisions, evidence, and limits that would otherwise disappear between meetings and requests.
Read my storyA durable knowledge-work habit
Better tools still need better thinking.
Build the context habit once. Carry it into every new model, project, meeting, and workflow.
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