My product method · AI-Native Product OS
Understand the system. Give it a useful job. Keep learning.
AI can make a first working version cheaper to build. It also creates outputs that need to be checked. I use five areas of responsibility and a repeatable development loop to connect the work to a customer problem, clear decision rights and evidence of what happened.
The Stack · what to take responsibility for
- Model
- What can it do?
Choose capabilities against task quality, privacy, latency and cost.
- Context
- What should it know?
Choose relevant knowledge, its provenance, access and permitted use.
- Orchestration
- What can it act on?
Define tool access, sequence, retries and the limits of each action.
- Governance
- What must hold?
Test acceptance criteria, enforce limits and observe failures.
- Human
- Who is accountable?
Set direction, weigh trade-offs and decide when a person must intervene.
The Loop · how to develop it
- Talk
Understand the problem with the people who experience it.
- Decide
Agree scope, success criteria, authority and stop conditions.
- Build
Make a small, testable version inside those boundaries.
- Observe
Inspect user feedback, outputs, failures and evaluation results.
- Iterate
Change the system and rerun the relevant checks.
Governance and human responsibility apply throughout. The checks and release gates depend on the consequences of getting the task wrong.
The framework is this way of thinking. The workspace is the installable GitHub toolkit. The courses teach it on Udemy and Coursera.
