AI Development notes
Hands-on notes for turning AI ideas into working product surfaces, MVPs, integrations, and user workflows.
AI Development essays
The full set, newest first. Each note is written to help you make a decision rather than to recap the news.
Category guide
From product hypothesis to a testable AI workflow
AI development decisions begin before a model is selected. A team needs a named user, a recurring job, representative inputs, a reviewable output, and a reason the workflow is worth changing. The articles in this category focus on that product boundary: what must be true for an AI feature to become useful software rather than a demonstration.
The practical sequence is to isolate the riskiest assumption, build the thinnest complete flow, and test it with real material. A complete flow includes the input, model or retrieval step, review experience, error state, and the decision that follows. It does not require every production integration, but it must expose enough of the workflow to learn where value and failure actually occur.
Use these guides when evaluating an AI MVP, choosing between custom development and an existing tool, or deciding which product constraints should be proved before a larger roadmap is funded.
Questions these guides should help answer
- Q1What is the smallest workflow that can produce a meaningful decision?
- Q2Which examples represent normal work, difficult work, and unacceptable failure?
- Q3What remains manual in the prototype, and what would production still require?
- Q4Which evidence would justify scaling, pivoting, or stopping?