An AI-powered MVP is still an MVP: the goal is to learn, not to build a production system on day one. What changes is how you test it — because AI outputs are non-deterministic, the quality bar is set by evaluation, not by a unit test that passes or fails.
Scope to one workflow, one model, one metric
Resist the urge to automate several things at once. Pick one workflow, one model (or API), and one success metric defined before the build starts. Everything else is a distraction for the first version.
Build vs. buy for the model layer
Start hosted. A managed API gets you to a working demo in days and lets you defer the cost and complexity of hosting, fine-tuning, and maintaining infrastructure. Move to a self-hosted or fine-tuned model only when the evals prove the hosted option can't meet the bar — not before.
The eval loop
Because outputs vary run to run, define a small set of golden examples — inputs with known-good outputs — and score every prompt change against them. Iterate the prompt and the retrieval until the pass rate holds across the set. This loop replaces the red-green test cycle you'd run on deterministic code.
- Write 10–30 golden examples that represent the real distribution of inputs.
- Score outputs on a rubric: correctness, completeness, safety, tone.
- Track pass rate across changes so you notice regressions, not just improvements.
Keep a human in the loop
For the first version, route every output through a person who can approve, edit, or reject it. This protects against the non-deterministic failure modes that unit tests can't catch and gives you labeled corrections you can later use to improve the system.
Instrument from day one
- Log every input and output for debugging and future fine-tuning.
- Track cost per run so the unit economics are visible early.
- Measure latency — slow AI is often worse than fast manual work.
- Flag low-confidence outputs for review rather than auto-publishing them.
Ship narrow, then widen
A narrow pilot that one team uses daily beats a broad feature no one trusts. Prove the workflow, the metric, and the unit economics on a small surface, then widen scope once the evals and the cost numbers hold up in real use.