TRAIL
Knowing if it works
Evaluation sets, benchmarks, vendor claims, and confidence — how anyone actually knows an AI system is any good.
“It seems better” is the most common quality signal in AI work, and it is not a signal at all. This trail is about replacing impressions with something repeatable — for your own systems, and for the ones you are being sold.
By the end you’ll have built a small evaluation set from real cases, know how to read a benchmark claim without over-trusting it, know what evidence to demand from a vendor, and know how to get a confidence signal that isn’t just the model sounding sure of itself.
Begin trail →- 1UnderstandHow evaluation works
The foundation: a fixed set of real cases with known good answers. Twenty of them will catch regressions that trying a few prompts never will.
- 2UnderstandWhat a benchmark measures
Public leaderboards measure something real and rarely the thing you need. Learn what a score actually covers, and what contamination does to it.
- 3UnderstandUncertainty and confidence
A system that can flag its own shaky cases is worth far more than one that sounds certain about everything — but the flag has to come from structure, not from tone.
- 4UnderstandSeeing inside a model
Why you can’t simply ask the system why it decided: a model’s self-explanation is generated text, not a readout of its internals. It’s the reason accountability has to come from records you keep, not the model’s own account of itself.
- 5JudgeVendor evidence
Now turn it outward: the specific evidence that should change a purchasing decision, and the answers that sound like evidence but aren’t.
- 6JudgeReality Lab: evidence
Finish hands-on: work through the evidence for a claim yourself, so “ask for evidence” stops being advice and becomes a practised move.