The working method
How we work with AI
AI is useful here because it can help explore options, build reviewable changes and catch problems. The question, direction, judgement and responsibility remain human.
Working rule
Use AI to increase the quality of your thinking, then inspect what it produces before you rely on it.
Five checkpoints
A simple collaboration loop
This is the public-facing method behind the project. It is intentionally understandable without exposing private records, prompts or internal systems.
- Human context comes first.
Start with the real goal, constraints and what you currently believe.
- AI expands the options.
Ask for alternatives, challenges, drafts or a bounded implementation that can be reviewed.
- Important claims are checked.
Use primary or reliable sources when accuracy, safety or cost matters.
- A human reviews and decides.
Test the result, correct weak assumptions and choose what should move forward.
- The learning is recorded.
Keep the useful decision, correction or milestone so the next step begins with better context.
Clear roles
What stays human and what AI accelerates
The line is not perfect, but making it visible is more honest than pretending either side worked alone.
Human contribution
Direction, judgement and accountability
- Choosing the goal and why it matters
- Providing lived context and preferences
- Reviewing wording, design and important claims
- Testing what reaches the live website
- Making the final call and owning the result
AI contribution
Exploration, implementation and checking
- Generating options and challenging assumptions
- Drafting reviewable text and code
- Running bounded checks and finding defects
- Organising evidence for human review
- Accelerating repetitive work without owning the decision
Practical boundary
AI output is a starting point, not proof
Good collaboration includes disagreement, corrections and uncertainty. If the evidence is weak, the honest label is unknown, unverified or planned.