AI Can Hurt Junior Engineers — A Simple Guide (and How to Avoid It)

AI Can Hurt Junior Engineers — A Simple Guide (and How to Avoid It)

Short version: AI can make you fast today but weak tomorrow if you let it think for you. Keep your brain in charge. Use AI as a helper, not as the driver.

Why this matters

When you are new, you grow by figuring things out: making a small repro, adding logs, testing ideas. If AI gives you fixes you don’t understand, your debugging muscle doesn’t grow. You ship code, but you can’t explain it or fix it when it breaks later.

Rule: Use AI to speed up things you already understand. Avoid using it to solve a brand‑new problem for you.

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How AI can quietly hurt your skills

1. You feel like you understand, but you don’t. Tests pass, but you can’t explain why.
2. Copy‑paste code with hidden traps. Timezones, encodings, rare edge cases bite later.
3. Random trial‑and‑error replaces a plan. No learning happens.
4. You stop thinking out loud. No notes, no steps, no reason → no growth.
5. Speed becomes the goal. You skip root cause analysis.
6. Confident tone without evidence. Looks pro, breaks in prod.

Real examples (simple and real‑life)

A) Frontend layout: images cause layout shift (CLS)

• What you saw: Page jumps when images load.
• AI said: “Use object-fit: cover and set heights with JS.”
• Why it failed: The browser had no reserved space. Layout changed after images decoded.
• Do this instead: Give images width/height or aspect-ratio so space is reserved.

<img src="hero.jpg" width="1200" height="600" style="aspect-ratio:2/1;object-fit:cover">

B) React state: stale closure

• What you saw: Duplicated items in infinite scroll, mostly in production.
• AI said: “Wrap in useCallback and debounce.”
• Why it failed: The callback used old state.
• Do this instead: Use a functional update or a reducer.

setItems(prev => prev.concat(newChunk));

C) Backend retries: accidental self‑DDoS

• What you saw: Service down; many retries.
• AI said: “Add retries.” (no jitter, no circuit breaker)
• Why it failed: All clients retried together and flooded the upstream.
• Do this instead: Exponential backoff with jitter, max attempts, circuit breaker.

D) Timezones: off‑by‑one day

• What you saw: Morning totals wrong by ~5%.
• AI said: “Store ISO strings.”
• Why it failed: Mixed UTC/local math; DST changed the hour count.
• Do this instead: Store & compute in UTC. Convert only at the UI. Add tests across DST.

E) TV apps: focus jumps on different brands

• What you saw: Arrow keys skip cards on Samsung but not LG.
• AI said: “Throttle keydown events.”
• Why it failed: Vendors send keys differently; DOM order ≠ visual order.
• Do this instead: Use roving tabindex with 2D focus math and vendor key maps.

Where AI is actually useful

• Boilerplate: types, configs, CI, simple CRUD.
• Reading help: Summaries of long docs (you still read the key parts).
• Question coach: Ask AI to ask you questions that narrow the bug.
• Test ideas: Edge‑case tests from your spec/repro.
• Diff explainer: “What changed here and what could break?”

If you can predict what the fix will change, AI can help you write it faster. If you can’t, build your understanding first.

Traffic‑light rules

• Green (safe): You know the pattern. AI saves typing.
• Yellow (careful): You know the system but not the API. Ask for questions and checklists, not code.
• Red (stop): New domain, prod incident, security, tricky concurrency. Don’t paste AI code. Use it only to plan your investigation.

Debugging habits that build skill

1. Reproduce the bug in a small, reliable way.
2. Measure before fixing: logs, counters, performance.mark, screenshots.
3. Write one clear hypothesis. “If X is true, we should see Y.”
4. Change one thing at a time. Keep short notes.
5. Shrink the search space: binary search code paths, feature flags.
6. Make a minimal repro. Tiny repo or failing test.
7. Keep the test after you fix it.
8. Then ask AI: “Here’s my repro and hypothesis — what else should I check?

Copy‑paste templates

Coach prompt for AI

Act as my debugging coach. Ask 5 sharp questions to narrow a React list duplication bug that appears only in production. Don’t give code yet.

Safety check before pasting AI code

• Do I have a failing test or repro?
• Can I explain how this affects state, layout, or I/O?
• What could break under load, timezones, or devices?
• Do I have telemetry to catch regressions?

What good PRs look like (vs bad ones)

• Bad smells: no repro, vague “fixes…”, huge generic code, no rollback plan.
• Good PR: clear problem, minimal repro link, before/after metrics, short RCA.

A simple 4‑week plan to get strong

• Week 1 — Observe: keep a bug journal; 1 hour/day no‑AI on small issues.
• Week 2 — Instrument: add tracing or performance.mark() to one flow.
• Week 3 — Reproduce: build a minimal repro for one flaky bug.
• Week 4 — Teach: write a short postmortem; use AI only to polish writing.

When to avoid AI completely

• Brand‑new domain + no tests.
• Live incidents.
• Security/privacy issues.

Use docs, draw diagrams, write the first failing test yourself.

Bottom line

AI isn’t evil. It’s a tool. If you let it replace your thinking, your debugging skills will fade. If you keep ownership of the repro, hypothesis, and measurement, AI will make you faster and smarter, not just faster.

If this helped, tap 💚 on Medium and share. I post deep dives like this every week — follow for more.

Happy coding 💻✨

✍️ By Vishal Solanki

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AI Can Hurt Junior Engineers — A Simple Guide (and How to Avoid It) was originally published in JavaScript in Plain English on Medium, where people are continuing the conversation by highlighting and responding to this story.