Executive Summary
Treat features as hypotheses. Measure. Learn. Repeat. Ship faster by getting less precious about being right.
Shipping is an experiment loop. Every feature is a hypothesis with a measurable prediction.
The fastest teams aren’t reckless—they’re disciplined about learning and ruthless about killing weak hypotheses.
“Production is where good ideas meet boring reality. The winners instrument the boring part.”AI & Dev Dispatch
The Core Idea
Most “AI failures” are system failures: missing contracts, missing logs, missing ownership lines. Fix the system, and the model suddenly looks smarter.
Contract
Define the stable input/output boundary first.
Logs
Capture raw facts, not just summaries.
Policy
Centralize allow/deny decisions and expose reason codes.
UX
Make failure legible and recoverable.
// Stable contract surface (gateway request)
POST /.netlify/functions/gateway-chat
{
"org_id": "...",
"user_id": "...",
"model": "gpt-4.1-mini",
"messages": [...]
}
That snippet is not a complete app. It’s a reminder: your system should prefer verifiable facts over narrative.
Failure Modes You’ll Actually See
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Vanity metrics
Track outcomes that matter, not dashboards that look impressive.
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No control group
Without comparison, you don’t know if you improved anything.
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One-way doors everywhere
Most decisions are reversible; treat them that way.
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No postmortems
If you don’t write down what happened, you’ll repeat it.
Implementation Notes
Write hypotheses with predicted outcomes and a measurement plan before building.
Ship smaller changes, measure sooner, and stop defending bad ideas with more code.
Keep a lightweight ‘experiment log’ so the team learns across cycles.
For architecture and rollout planning, use the Contact Hub.
Ship‑Ready Checklist
Use this as a pre‑deploy gate. If you can’t check these boxes, don’t pretend you’re “done.”
- A single source of truth for versions (prompt/policy/schema) and a way to display them in-app.
- Request correlation ID visible in UI, logged server-side, and searchable.
- Explicit failure UX: what happened, why, and a safe next step.
- An audit trail you can replay: inputs, decisions, outputs, and cost facts.
- A small test harness (even 20 cases) that runs before deployment.
Further Reading
External references (full links):
Related Reads in This Series
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