An AI Evaluation Plan Before Deployment

A demonstration can look impressive without establishing that a system works reliably. Evaluate the intended use before measuring success. A useful evaluation starts with a clear boundary: who will use the output, what decision could follow, and which failures would matter most. The purpose of this guide is to help teams plan a small, reproducible evaluation rather than mistake a prototype for a finished service.

Define the task and its boundaries

Write down input types, expected outputs, who reviews the result, and what the system must not do. Select realistic examples and include difficult or ambiguous cases. An assistant that drafts explanations needs different evidence from a service that takes actions on behalf of a customer.

Measure quality with representative samples

Separate factual correctness, relevance, completeness, clarity, and harmful mistakes into different review fields. Record failures rather than keeping only successful demonstrations. A reviewer should be able to reproduce the input and understand why a result passed or failed. Avoid claiming accuracy from a few hand-picked examples.

Control deployment and monitoring

Make human approval explicit for consequential or irreversible actions. Define ownership, time limits, rollback, and ways to report unexpected output. Review performance again when content, users, integrations, or operating conditions change. A passing test today does not guarantee safe behavior indefinitely.

Practical exercise

Take five real examples from your intended workflow and add three examples where a confident answer could still be wrong. Create a review sheet with separate fields for relevance, evidence, and the action a reader might take. Ask a second reviewer to assess the same cases without seeing the first reviewer’s scores. Compare disagreements before changing any automated threshold; disagreement is useful evidence of an unclear evaluation question.

Scope and limitations

This is a planning guide, not a certification, safety guarantee, or report on a deployed MojPlayWin AI model. Thresholds require task-specific evidence and expert assessment.

Reference frameworks

https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10

https://www.nist.gov/itl/ai-risk-management-framework/nist-ai-rmf-playbook

Educational material: this is a guide, not a verified claim that MojPlayWin has deployed these controls. Check linked primary sources before applying the advice.