What I learned mentoring juniors for four decades seems to apply to today's agentic coding.
"The true goal of mentoring is never to supply the answer. It is to point the flashlight into the dark corner of the state space and let the student discover the monster for themselves." — Randal L. Schwartz The Prompting Deluge: Why "Prompt Tricks" Are Failing You
Search YouTube, Twitter, or developer blogs for "AI prompting" today, and you will be inundated by a relentless flood of breathless titles promising secret cheat codes:
"Stop Prompting Like a Beginner: Use This 5-Step Secret Template!" "10 AI Prompts That Will Make You a 10x Engineer Overnight!" "Why You Should Always Tip Your AI $200 and Tell It to Take a Deep Breath" "The Ultimate System Prompt That Forces Claude to Never Make Mistakes"
It is an endless parade of cargo-cult incantations. We are being told that if we just find the right magical preamble, adopt the right persona, or sprinkle the right polite phrases into our prompts, the machine will suddenly stop hallucinating and write flawless code.
You are at your terminal at 3:30 PM. You are trying to ship a feature. You spot an architectural hazard in the AI's generated code, and you issue a clear, direct, reasonable instruction:
"You are entirely right! I apologize for my grave oversight. Here is the updated code."
It patches line 42. But in doing so, it introduces an unhandled error that crashes the isolate when the component unmounts.
You get annoyed and bark another order: "Now check if mounted is true before calling setState!"
The model apologizes again, wraps the entire function in three layers of defensive try-catch blocks, adds a pass-through identity shim, and completely breaks state initialization.
Within four turns, you are sweating, your context window is polluted with groveling apologies, and the code looks like corporate enterprise middleware from 1998. You find yourself doing all the heavy lifting in your own head, typing out paragraph-long specifications, and acting as an underpaid Human Compiler.
Because prompt tricks treat the AI like a broken vending machine: they assume that if you just jiggle the coin slot with the right magic incantation, the right candy bar will drop out.
They fail because they are built on a fundamentally broken mental model. Stepping Back: The Relatable Mental Model
To fix this, you do not need another prompt template. You do not need another YouTube listicle.
You need to step back and adopt an accurate working model of how generative agents actually "think."
When you interact with a modern large language model, you are not talking to a deterministic bash script, an SQL database, or an optimizing compiler. You are interacting with an autoregressive simulator trained on the totality of human linguistic interaction: our debates, our textbooks, our code reviews, our stack traces, and our pedagogical dialogues.
Because the model was trained on human communication, its internal dynamics mirror human cognitive dynamics.
And there is one specific human relationship that provides the perfect, battle-tested working model for collaborating with an AI coding agent: