In the early 1960s, a doctor couldn’t just order a lab test. They were manual and expensive and you ordered one after you reasoned out what the problem might be.
Then in 1967, the SMA-12 was invented and changed everything: 12 different tests simultaneously, results on a single sheet of paper, in less than a minute.
A quieter habit - shotgun testing - crept in.
Why hypothesize when you could run a full panel on everyone who walked in?
The result wasn’t clarity but a flood of false positives. A doctor was now looking at dozens of pages showing minor, irrelevant deviations and chasing one rabbit hole after the next as they ordered more tests.
The medical field saw this and set up frameworks that re-injected the friction that the machine had removed. No panel could be run unless the patients’ symptoms justified it.
Today, you are no different.
The most powerful diagnostic instrument ever made sits in front of you and asks “How can I help you today?” and you shotgun it.
Vague prompts, chasing rabbit holes, and when you are tired, grab what it flags.
Everyone calls it AI workslop, output that is unfocused and not actionable.
However, what no one names the underlying cause that’s burning through your tokens and burying the real signal in the noise.
This week, we close that gap.
No its not a discussion on prompt engineering or a list of prompts to follow but a framework that helps you implement structural (work-related) and behavioral changes as you use your AI tools.
This way, you pull in sharper answers, drown in less noise, and run leaner agents that cost a fraction to operate.