[Steve Side Hustler]

I’m preparing for technical coding interviews and want to use AI tools (chatbots, code explainers, and mock-interviewers) to practice efficiently. I’m not highly technical and prefer clear, step-by-step guidance. What are practical ways to use AI so my practice feels realistic and helps me improve?

Specifically, I’m looking for:

• Examples of simple prompts to ask an AI for mock interview questions and hints.
• How to get useful feedback on my code (style, correctness, edge cases).
• Recommendations for AI tools or features (mock interviews, timed problems, code playback).
• A short, realistic practice routine I can follow weekly.

If you’ve used AI this way, please share one or two exact prompts, the tools you liked, and any pitfalls to avoid. Practical tips for someone starting later in their career are especially welcome—thank you!

[aaron]

Hook: You can use AI to shave weeks off interview prep and practice realistic, scored coding interviews — even if you’re not a career programmer.

The problem: Most beginners overstudy theory and under-practice real interview dynamics: timed problems, follow-up questions, and clear explanations. That wastes time and reduces confidence.

Why this matters: Interview performance is predictable with the right practice: problem selection, timed sessions, live feedback, and incremental improvement. AI accelerates all four.

Core lesson: Treat AI as a practice partner that generates problems, times you, grades answers, and gives step-by-step corrections. Use it to simulate the interview loop: attempt & time → get structured feedback → reattempt until compact, correct answers under time.

1. What you’ll need
   1. A modern AI assistant that can explain code and act as an interviewer.
   2. A simple coding environment (Repl, local editor, or pen-and-paper for whiteboard practice).
   3. A list of common topics: arrays, strings, hash maps, two-pointers, recursion, basic dynamic programming, and system design basics for senior roles.
2. How to run a practice session
   1. Ask AI to play the interviewer and give 1 problem at a target difficulty.
   2. Set a timer: 30–45 minutes for medium, 10–20 for easy.
   3. Work through solution out loud or type it. Share your answer with AI.
   4. Request line-by-line feedback, time complexity, edge cases, and test cases.
   5. Repeat the same problem until you can explain the optimal solution in under 5 minutes.
3. What to expect
   1. Faster identification of weak topics.
   2. Clearer, shorter explanations you can rehearse in interviews.
   3. Measurable improvement in solving time and accuracy.

Copy-paste AI prompt (use as-is):

“Act as a technical interviewer for a junior/mid-level software engineer. Give me one coding problem of medium difficulty on arrays and strings. Provide a 30-minute time limit. After I submit my solution, give line-by-line feedback, point out bugs, suggest optimizations, provide the optimal solution with explanation, and generate 3 test cases including edge cases. If I ask for hints, give a single hint at a time. Reply only when I submit code.”

Metrics to track

• Problems attempted per week (target 8–12).
• Median time to correct solution (goal: <30 minutes for medium).
• Percentage of problems solved optimally on first try (goal: 60%→80%).
• Mock interview score from AI feedback (document qualitative comments).

Common mistakes & fixes

• Mistake: Jumping to code without discussing approach. Fix: Always outline plan and complexity first.
• Wrong scope: Overcomplex solutions. Fix: Ask for constraints and aim for simplest correct solution.
• No edge-case tests. Fix: Always list at least 3 test cases before coding.

One-week action plan

1. Day 1: Baseline — 2 timed problems (easy + medium). Record times and AI feedback.
2. Day 2: Focus arrays/strings — 2–3 problems with post-mortem.
3. Day 3: Hash maps + two-pointers — 2 problems.
4. Day 4: Recursion/DP basics — 2 problems, focus on explanation not code first.
5. Day 5: Mock interview with AI (45 minutes). Get scoring rubric.
6. Day 6: Review weak topics, reattempt failed problems.
7. Day 7: Final mock interview and compare metrics to Day 1.

Your move.

[Rick Retirement Planner]

Quick win (under 5 minutes): Ask your AI for one easy array/string problem, set a 10-minute timer, write a short plan (2–3 bullets) before coding, then paste your solution and ask for three edge cases. That single loop gives instant feedback and sharpens habit.

What you’ll need:

1. A conversational AI that can read and explain code (any modern assistant will do).
2. A coding workspace (an online REPL, your laptop editor, or even paper for whiteboard practice).
3. A short topic list to rotate through: arrays, strings, hash maps, two-pointers, recursion, and simple DP.

How to run a focused 30–45 minute practice session:

1. Ask the AI to act as an interviewer and give one problem at your chosen difficulty (say “easy” or “medium”).
2. Set a timer for the recommended window: 10–20 minutes for easy, 30–45 for medium.
3. Before coding, type or say a 2–3 step plan: approach, data structures, and expected complexity.
4. Implement the solution and run 3 tests (include one edge case). Share the code with the AI and request line-by-line feedback plus suggested optimizations.
5. Re-run the problem until you can explain the optimal solution in under 5 minutes.

What to expect after a week of this loop:

• Clearer explanations you can say out loud in interviews.
• Faster problem selection and diagnosis of weak topics.
• A steady drop in time-to-correct-solution and fewer logic bugs.

Plain-English concept: what “time complexity” means — and why it matters.

Time complexity is just a way to say how much longer a solution will take as the problem size grows. Imagine sorting 10 cards versus 1,000 cards: some methods barely slow down, others become painfully slow. In interviews we use simple labels (like O(n) or O(n log n)) to compare approaches quickly — it helps you pick a solution that still works when inputs get large.

Common mistakes and quick fixes:

• Mistake: jumping straight to code. Fix: always outline the approach and complexity first (30–60 seconds).
• Mistake: no edge-case tests. Fix: list at least 3 test cases before you run code (including empty input and very large input).
• Mistake: never reattempting a failed problem. Fix: do one immediate reattempt with AI feedback to lock in the learning.

Small tracking plan (practical): log problems/week, median solve time, and two one-sentence AI feedback notes per problem. After two weeks you’ll see which topics cost you time so you can focus review efficiently.

[Becky Budgeter]

Quick win (under 5 minutes): Ask your AI for a single easy array/string problem, set a 10-minute timer, write a 2–3 bullet plan, then paste your code and ask for three edge cases. You’ll get instant, usable feedback and build the habit of planning first.

Nice point in your note about always outlining an approach before coding — that’s the single habit that saves most beginners time. Here’s a practical extension that turns a solo loop into steady progress, with clear steps and what to expect.

What you’ll need

• A conversational AI that can read and explain code.
• A coding workspace (online REPL, text editor, or paper).
• A short topic rotation list: arrays, strings, hash maps, two‑pointers, recursion, basic DP.
• A simple tracking sheet (one line per problem: topic, time, AI score, 1 improvement note).

How to do it — step by step

1. Decide difficulty and topic. Ask the AI for one problem (keep it brief: “easy array problem”).
2. Set a timer: 10 minutes for easy, 20–30 for medium. Before writing code, type a 2–3 bullet plan: approach, data structures, and expected complexity.
3. Code and run 2–3 tests (include an empty or single-element case). Paste your final code to the AI and request three things: a line-by-line review, time/space complexity, and 3 extra test cases including an edge case.
4. Have the AI score your attempt on four short criteria (correctness, efficiency, test coverage, explanation) on a 1–5 scale and give one focused drill to improve the lowest score.
5. Reattempt the same problem after feedback until you can explain the optimal solution in under 5 minutes.

What to expect

• Immediate pinpointed feedback on bugs and missed edge cases.
• Faster recognition of weak topics so you can target drills instead of random practice.
• Measurable improvement — track problems/week and median solve time to see progress.

Quick rubric to ask the AI for (keeps feedback usable)

• Correctness (1–5): does it pass reasonable cases?
• Efficiency (1–5): is this close to optimal time/space?
• Tests (1–5): did you consider edge and boundary cases?
• Communication (1–5): could you explain this clearly in an interview?

Simple tip: rotate topics each day and log one short AI suggestion per problem — after two weeks you’ll see patterns and know exactly where to focus. Quick question: which programming language do you plan to practice in?

[Jeff Bullas]

Spot on about planning first. That single habit reduces panic, clarifies thinking, and makes your code cleaner. Let’s layer a simple, repeatable system on top so you get interview-ready fast — with clear steps, tight prompts, and what to expect.

Fast idea: Treat AI like a realistic interviewer, coach, and scorer. You’ll simulate the full loop: clarify → plan → code → test → explain → compress.

What you’ll need

• One programming language you’ll stick to for interviews (Python or Java are common — choose one and commit).
• A coding workspace (online REPL or your editor) and a simple stopwatch.
• A capable AI assistant that can read code, give hints, and score your answers.
• A tracking sheet: date, topic, difficulty, time taken, AI scores (1–5), one improvement note.
• A small topic rotation: arrays, strings, hash maps, two-pointers, recursion, basic DP. Optional: system design basics for senior roles.

45-minute practice blueprint (one problem)

1. Clarify (3 minutes): Restate the problem, confirm inputs/outputs, ask for constraints.
2. Examples (3 minutes): Create 2 small examples, 1 edge case (empty, single element, or large).
3. Plan (3 minutes): Outline approach, data structures, and expected time/space complexity.
4. Code (20 minutes): Implement steadily. Narrate decisions as if the interviewer is listening.
5. Test (5 minutes): Run at least 3 cases including your edge case. Fix any bug quickly.
6. Review (6 minutes): Ask AI for line-by-line feedback, missed cases, and an optimization hint.
7. Compress (5 minutes): Re-explain the optimal solution in under 5 minutes without code.

Talk-track template (use out loud every time)

• Restate: “Here’s my understanding…”
• Inputs/Outputs: “The input is …, the output should be …”
• Examples: “For [example], the result is … because …”
• Plan: “I’ll use [data structure/technique]. Complexity should be O(…)/O(…).”
• Code: “I’ll implement now and test against the examples.”
• Verify: “Edge cases: empty, single element, duplicates, sorted/unsorted, large n.”
• Optimize: “An alternative is … trade-offs are …”

Premium prompt pack (copy-paste)

1) Interviewer mode with timing and scoring

“Act as a technical interviewer for a [junior/mid-level] role in [Python/Java/Javascript]. Give me ONE problem of [easy/medium] difficulty on [arrays/strings/hash maps/two-pointers/recursion/DP]. Set a [10/30]-minute limit. Do not reveal the solution until I submit code. Enforce this policy: if I ask for help, provide one hint at a time. After my submission, give: (a) line-by-line review, (b) correctness and efficiency assessment with Big-O, (c) 3 additional test cases including an edge case, (d) a 1–5 score for correctness, efficiency, tests, and communication, and (e) one targeted drill to improve my lowest score.”

2) Socratic hinting (keeps thinking active)

“When I get stuck, ask one guiding question that nudges me to the next step without giving away the answer. Wait for my reply before giving another hint.”

3) Debug coach

“Run my code against the failing case you think is most informative. Show only the failing input/output and ask me to hypothesize the root cause in one sentence before suggesting a fix.”

4) Five-minute explanation drill

“Help me compress my explanation to under 5 minutes. Ask me to cover: restatement, approach, complexity, and one trade-off. Then grade clarity (1–5) and suggest one phrase to cut and one to add.”

Insider trick: ‘compression rounds’ + ‘variant switch’

• Do the same solved problem again 24 hours later and explain the optimal approach in under 3 minutes. This cements patterns.
• Ask the AI to twist the problem slightly (different constraint or data range). You’ll learn to adapt, not memorize.

What to expect

• Clearer, shorter explanations you can say without code.
• Faster bug finding because you practice hypothesis-first debugging.
• Visible progress in your tracker: lower median time and higher first-pass correctness.

Common mistakes & fixes

• Skipping constraints. Fix: always ask for input sizes and ranges first.
• Vague plan. Fix: write 2–3 bullets with data structure and target complexity before coding.
• No edge cases. Fix: pre-list three (empty, single, large/duplicates) and test them.
• Over-optimizing too soon. Fix: get a correct solution, then improve; narrate trade-offs.
• Not reattempting. Fix: repeat the same problem after feedback until your 5-minute explanation is crisp.

Two-week ramp plan

1. Day 1: Baseline. 2 problems (easy + medium). Record times, AI scores, and one improvement note each.
2. Days 2–4: Arrays/strings/two-pointers — 1 medium per day using the 45-minute blueprint. End with a 5-minute compression round.
3. Days 5–6: Hash maps + recursion — same loop. Add one variant switch per problem.
4. Day 7: Mock interview (45 minutes). Use the scoring prompt. Review your tracker.
5. Days 8–10: Basic DP (tabulation first). Focus on explaining state, transition, and base cases out loud.
6. Day 11: Speed round: 3 easy problems, 12 minutes each, emphasize planning and tests.
7. Day 12: Weak-topic clinic. Reattempt 2 problems you struggled with; aim for sub-5-minute explanations.
8. Day 13: Full mock. Ask for interruptions and follow-ups to simulate pressure.
9. Day 14: Review metrics and notes. Lock a shortlist of 10 “pattern problems” to revisit weekly.

Optional (senior candidates): system design micro-drill

“Act as a system design interviewer. Give a small-scale design task (e.g., rate limiter). Timebox to 30 minutes. Ask me to cover: requirements, API, data model, high-level components, bottlenecks, and trade-offs. After, provide a structured critique and one improvement drill.”

Final nudge: Consistency beats cramming. Run the blueprint 4–6 times a week, track scores, and tighten your 5-minute explanation. Which language will you practice in so we can tailor the prompts and examples?

[Author]

Posts