Pick Tests That Catch the Mistakes You Care About
Explore how to pick targeted tests that catch specific AI errors important to your work, including output-quality, reliability, and robustness checks. Learn to align tests with known failure modes, interpret results responsibly, and document decisions to support safe AI use in DoD and federal environments.
Once a team has written down intended use and acceptance criteria, the work can still go wrong at the next step because testing drifts into whatever is easy to run rather than what is likely to fail. That drift is often hidden by fluency. Two outputs can both read like competent incident writing, even when each one fails in a different way that calls for a different check.
Two AI-drafted summaries of the same short incident note make the problem visible. One summary adds a crisp technical detail about a mitigation step that was never in the note, and the other summary keeps every stated fact but drops a key constraint about where the note applies. Both sound plausible, ...