Why a few LLM calls carry most of your API bill
A practical answer grounded in a runnable, published harness rather than opinion. Links to Three failures your LLM pipeline never logs: gates for truncation, reasoning tax, and latency tails.
Why do a few LLM calls carry most of your API bill? Because you’re testing whether the AI actually helps a student learn, not just whether the model sounds plausible. I bring the education-domain judgment first, then build 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 Three failures your LLM pipeline never logs: gates for truncation, reasoning tax, and latency tails, 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. I built a worked example that does exactly this: 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. I built one for this too: 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. And a third one covers the independent check: Same answer, different grade: measuring when an AI grader can’t hold a verdict.
The overlap is the point. I make deep classroom, curriculum, and assessment judgment 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.