TOPIC 01 OF 06
A demo is not evidence
Impressions vs. measurementThe most common mistake with AI is deciding it works from a handful of lucky prompts. A model's output is variable, and the failures often hide inside the fluent, confident ones. Evaluation is the discipline of checking — in a structured, repeatable way — whether a system does what you need, on cases you chose in advance, so 'it feels good' becomes something you can actually stand behind.
You try a summariser on three articles and it nails them, so you roll it out. On the fourth — a legal notice — it quietly drops the one clause that mattered. Three good demos told you nothing about the case that would hurt you.
Fluency is uniform; correctness is not. A system that sounds equally confident on its right and wrong answers is exactly the one you can't judge by impression — you have to measure it.
Take one AI use you rely on and write down how you currently 'know' it works. If the answer is 'it seemed fine,' that's the gap this studio closes.