An AI MVP must prove more than a prompt
A useful AI MVP tests whether a model-assisted workflow creates value at an acceptable quality, cost, and risk level. A polished chat screen alone does not answer that question.
Minimum scope
- one defined user and outcome
- representative input data
- a baseline or current alternative
- an evaluation set with expected results
- source traceability where factual answers matter
- explicit tool permissions
- human review for high-impact actions
- logging, cost visibility, and failure handling
Model choice
Use current provider documentation and benchmark the real task. Do not hard-code the article around one provider or an old model generation.
Production boundary
Keep deterministic code around identity, authorization, billing, data changes, and irreversible actions. The model may propose or classify; the application decides whether the action is allowed.
What the MVP should learn
Measure whether users complete the workflow, whether quality meets the agreed threshold, how often humans intervene, what each successful outcome costs, and which failures remain.
A production AI MVP is a small product with measured uncertainty. It is not an autonomous agent with broad permissions and no evidence.
Fact-check sources
- NIST AI Risk Management Framework
- OWASP Top 10 for LLM Applications
- OWASP Top 10 for Agentic Applications 2026
Sources and product documentation can change. Recheck time-sensitive pages on the publication date.