Designing scalable AI MVPs

An AI MVP should prove one user outcome, not showcase every model feature. Pick a narrow workflow, define success metrics, and build the smallest loop: input → model call → human-visible result → feedback.

Separate prompt and model configuration from application code so you can iterate without redeploying the whole stack. Log prompts and outputs (with privacy in mind) from day one—you need traces to debug quality regressions.

Plan for scale as constraints, not premature optimization: stateless API workers, object storage for uploads, and a queue before you need it. If the MVP works, you extend observability and caching; if it does not, you have not overbuilt.

When hiring, ask vendors for a written MVP scope: models, platforms, analytics, and store submission. Ambiguity there becomes change orders later.