A Rule-First Approach to AI-Assisted Terminology QA
Terminology errors are rarely dramatic. They usually appear as small inconsistencies: one deprecated term, one preferred phrase that was missed, one verbose expression that should have been simplified, or one word that does not match the approved glossary. In technical documentation, these small issues matter. They affect consistency, readability, translation quality, and sometimes even product safety. The problem is that terminology checks are repetitive. They are also easy to miss when reviewing long documents manually. That makes terminology QA a good candidate for automation. But there is an important design question: Should AI be the first checker? My answer is: usually, no. For production-oriented QA, terminology checking should start with explicit rules. AI should be used as a second-pass confirmation layer, not as the primary source of detection. This is the design idea behind a small terminology checker I recently built and published on GitHub. The reposi...
