prompt engineering

Models read the start and end of your context far better than the middle, a measured effect with direct consequences for chunk ordering, k, and prompt layout. What to change and how to prove it.

Prompts are production code that nobody treats like code, until an innocent one-line tweak tanks accuracy. The version-eval-canary-rollback pipeline that AI-native interviewers expect by default.

This is the live practical several labs run, and it is scored on protocol more than on the prompt you end with. Read all thirty before editing, cluster them, hold some out, change one thing at a time, and report with intervals, because 24 of 30 fixed is somewhere between 63% and 91%.