TL;DR — AI can write the code, but it can't build your mental model for you. These five habits — read the diff before merging, explain it back, probe the trade-offs, track your concepts, and revisit under pressure — turn AI-assisted work into understanding you keep. None of them require slowing down.
Why this is a new problem
Learning by writing code used to be automatic: you couldn't ship what you couldn't reason about. AI assistance breaks that link. The code compiles, the tests pass, the PR merges — and none of it required you to understand why the solution works.
Closing that gap is now a deliberate practice. Here's what works.
Read the diff before you merge
The single highest-leverage habit. Before accepting an AI change, read it as if you were reviewing a teammate's PR you'll have to maintain. Ask: what does each hunk do, and what would break if it were wrong?
If you can't explain a hunk, that's the exact spot to slow down — not the whole PR.
Explain it back in one sentence
For each meaningful change, force yourself to summarize the reasoning in a sentence: "We retry with exponential backoff here because the upstream rate-limits bursts." If the sentence won't come, the concept hasn't landed yet.
This is also the fastest self-check: explanation is a much stronger signal of understanding than recognition.
Probe the trade-offs
Working code hides the decisions that produced it. Ask the ones the diff doesn't show:
- Why this approach and not the obvious alternative?
- What breaks at 10× the scale?
- What's the failure mode, and how would we notice?
You don't need exhaustive answers — you need to know which questions you can't yet answer.
Track concepts, not tasks
Tasks are done and forgotten. Concepts compound. Keep a lightweight record of the ideas you keep bumping into — caching strategy, auth token rotation, DB migrations — and your honest confidence in each. Over time that record becomes a map of where your understanding is strong and where it's thin.
This is exactly what a knowledge map does at team scale, and it's the backbone of Engineering Intelligence.
Revisit under pressure
Understanding you never test decays. Periodically revisit a concept in a low-stakes setting — before you're forced to during an incident. Spaced, evidence-based review beats re-reading, because it surfaces the gap while it's still cheap to close.
How do you review AI-generated code from Claude Code or Cursor?
The habits above hold regardless of tool, but agentic assistants like Claude Code and Cursor raise the stakes: they can change many files from a single prompt, so the diff you're reviewing is larger and further from your own reasoning. Two adjustments help:
- Review by concept, not by file. Group the change into the two or three ideas it actually implements and check that you understand each one — rather than scrolling file-by-file until fatigue sets in and you start skimming.
- Ask the agent to explain its trade-offs, then verify. "Why exponential backoff and not a fixed retry?" is a fast prompt, but treat the answer as a hypothesis to confirm against the code, not as the check itself. The understanding has to end up in your head, not just in the transcript.
How much should you slow down for AI code?
Less than you'd fear. The goal isn't to re-derive every line — it's to spend your attention where understanding is thin. Read every diff, but slow down only on the specific hunks you can't explain. For most changes that's a minute or two; for the one gnarly concept in a PR, ten minutes is a bargain against the incident it prevents. Blanket "review everything carefully" advice fails because it's unsustainable; targeted attention is the version you'll actually keep doing.
Putting it together
None of these habits require abandoning AI assistance or shipping slower — they run alongside the work you're already doing. Cartara automates the tedious parts: generating the explanation from your real diff, running the three-minute check, and maintaining the concept map so you can see understanding compound instead of guessing at it.
For the bigger picture on why this matters for teams, start with the pillar guide on Engineering Intelligence.