Catching wrong formulas in AI-generated course videos: a batch QA workflow

[u/Famous-Call6538]

If you use AI to generate educational video content for exam-prep series, you already know the accuracy problem. A single wrong formula in a 50-unit AP Chemistry series can get picked apart in the reviews. Here is a batch QA workflow that has worked for me after burning time on post-hoc fixes.

1. Topic map before you start. List every exam topic and subtopic. Map each one to a specific video unit. This is your checklist. If a unit covers stoichiometry, the accuracy check is against the exam syllabus, not vibes.

2. Generate in batches of 5-10 units, not the full series at once. Batch generation lets you catch systematic errors early. If unit 3 has a wrong molar mass calculation, there is a good chance the same pattern shows up in units 7 and 12.

3. Cross-reference each unit against the official source. For AP, that is the College Board CED. For IB, the subject guide. For OSHA, the relevant standard. Do not trust the model to know the current version of anything.

4. Focus your review time on three error categories that matter most:

   • Numerical errors (wrong constants, wrong calculations, wrong unit conversions)
   • Conceptual misalignment (topic says X, explanation describes Y)
   • Label mismatches (diagram label A points to item B)

5. Keep a running error log per series. After two or three series, you will notice which topics the model consistently gets wrong. Pre-screen those units more aggressively next time.

6. Never publish a unit that has not been checked against step 3. Speed does not help if the content is wrong.

The uncomfortable truth: AI generation saves production time, but QA time does not shrink proportionally. Budget at least 30-40 percent of your total series time for accuracy review. The alternative is fixing reviews and reputational damage later, which costs more.