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Solving vs Understanding in AI-Assisted Coding

3 min read
  • ai
  • learning
  • junior-developer
  • interview

The function works. The test is green. The ticket moves to Done.

So you must understand it… right?

Not necessarily.

Solving means a correct outcome exists. Understanding means you can recreate the reasoning without the scaffolding that produced the outcome.

AI tools collapse the distance between those two. That is the feature. It is also the risk.

Two different games

Solving Understanding
Goal Make it work now Be able to do it again
Signal Green tests, merged PR Clear explanation, transfer to a variant
AI's strength Extremely high Zero, unless you force the work
Interview cares about Sometimes Almost always

Juniors get rewarded for solving all day (ship, ship, ship). Interviews suddenly score understanding. The mismatch feels personal. It is usually structural.

The illusion of competence

Psychologists call a close cousin of this the illusion of explanatory depth: we feel we understand something until we have to explain it.

AI accelerates that illusion.

You watch a plausible solution appear. You skim it. It looks like what you "would have written." You accept. Your brain files the episode under "I know this" because recognition is cheap and reconstruction is expensive.

Recognition is not skill. Reconstruction is.

If you cannot rebuild the idea with a blank editor and a mild time pressure, you recognised a solution. You did not own it.

A five-minute check after every AI win

Use this whenever ChatGPT, Copilot, or Cursor just saved you:

  1. Close the chat. No peeking.
  2. Name the core idea in one sentence ("walk the array once and keep a running max").
  3. List one edge case the solution must handle.
  4. Change one constraint (unsorted → sorted, count → exists, array → string) and say what breaks.
  5. Re-type the critical five lines from memory. Not copy. Type.

Fail any step? You solved. You did not understand — yet. That is fine. Now you know what to practise.

Why "just do more projects" fails

Portfolio projects are still solving games if AI writes the hard parts. A beautiful repo with zero explainability is a demo, not evidence.

Interviewers have adapted. They open your code and ask:

  • Why this library?
  • What happens if this field is null?
  • How would you test the failure path?

Projects still matter. Narratable projects matter more.

How Prepzio frames the gap

Prepzio does not ban AI. It measures whether you still need it for the same class of problem next week.

Short sessions. Hint ladders instead of answer dumps. Feedback on reasoning. A simple trend: are you getting more independent, or just faster at prompting?

If that is the gap you feel after every green test, you are the reader this product is for.

Join the waitlist

Save your seat for the beta. First skill check when we open. No recruiters. No spam.

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