Red teaming llm applications
A practical answer grounded in a runnable, published harness rather than opinion. Links to Red-teaming my own publish gate.
I test whether an AI helps students learn. That’s different from testing whether the model just sounds plausible, and it’s what red teaming an LLM application means to me. I lead with education-domain judgment, then use runnable AI-quality harnesses to make that judgment measurable. This page walks through the method and links to one you can point at your own system.
This is the method behind Red-teaming my own publish gate, which you can read and run.
1. Make the failure observable.
Pick the specific way this can go wrong and build the smallest input that triggers it. If you can’t make it fail on purpose, you can’t prove it works. A worked example: Can you trust the model that grades your content? Measuring when an AI judge waves through broken work.
2. Measure against a baseline, not a vibe.
Compare to a neutral control so the number means something. A score with nothing to compare it to is theater. A worked example: Measuring feedback integrity: a blind-solver that catches AI explanations leaking the answer.
3. Check it a second, independent way.
Re-run with a different model family or a held-out set. Agreement across independent checks is the only verdict worth trusting. A worked example: Same answer, different grade: measuring when an AI grader can’t hold a verdict.
The overlap is the point: classroom, curriculum, and assessment judgment made measurable with AI/eval tooling. Every claim here maps to a public, reproducible harness, not a slide. If you want this run against your own system, the method transfers directly.