Launching an AI Toolkit With Confidence
A checklist for delivering production-ready AI features without over-engineering the stack.
Jan 5, 20251 min read
Shipping an AI-powered feature is about more than plugging an API key into a prototype. In client work I focus on three loops: discovery, experimentation, and delivery.
Discovery loops
- Start with a sharp, measurable problem statement.
- Map the data terrain early so surprises surface before sprint three.
- Align success metrics with the teams who actually feel the outcome.
Experimentation loops
Every experiment should earn its way into the roadmap. I score candidates with a lightweight rubric: impact, confidence, effort, and narrative value.
const experimentScore = (impact: number, confidence: number, effort: number) => {
return Math.round(((impact * confidence) / effort) * 100) / 100;
};
Delivery loops
- Treat evaluation like production-version the prompts, track the metrics.
- Instrument feedback so humans stay in the loop.
- Automate "boring" guardrails: monitoring, retries, rollbacks.
These rhythms keep teams shipping confidently even as the model landscape shifts.