How to choose an LLM provider when many serve the same model
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.
How do you choose an LLM provider when many of them serve the same model? You test whether the AI actually helps students learn, not whether it sounds plausible. I lead with education-domain judgment, then make that judgment measurable with runnable AI-quality harnesses. 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. Read it, run it yourself.
1. Make the failure observable.
Pick the exact way this can break, then build the smallest input that triggers it. If you can’t make it fail on purpose, you can’t prove it works. This one shows what that looks like: 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 the result to a neutral control so the number means something. A score with nothing to compare it to is theater. Here’s what that looks like in practice: Measuring feedback integrity: a blind-solver that catches AI explanations leaking the answer.
3. Check it a second way, independently.
Re-run it with a different model family, or a held-out set. Agreement across independent checks is the only verdict I trust. This one puts that to the test: Same answer, different grade: measuring when an AI grader can’t hold a verdict.
The overlap is the point. I bring deep classroom, curriculum, and assessment judgment, and I back it with AI and eval tooling that makes that judgment measurable. Every claim on this page maps to a public, reproducible harness, not a slide. Want it run against your own system? The method transfers directly.