Coding Interview Practice Workflow:The 4-Loop Method
January 30, 2026
TL;DR
Most coding interview practice fails because “solved” feels like the finish line. The 4-loop method forces what interviews actually grade: starting from blank, defending correctness, recalling the pattern later, and explaining it out loud. For each problem, do Attempt → Review → Redo → Explain. Attempt builds momentum, review adds edge cases and trade-offs, redo turns recognition into retrieval, and explain makes your reasoning sound clear under time pressure. Use AI only after your attempt (tests, edge cases, follow-ups), then close it and redo from memory. You’ll do fewer problems, but each one will transfer.
Introduction
If you’ve ever said “I did the problems, but I still froze,” you’re not alone.
A lot of practice stops at the moment the code passes. It feels productive… and then a real interview asks you to start cold, handle a twist, and explain why your approach is correct. That’s when “I solved it once” doesn’t help.
The fix usually isn’t more volume. It’s a workflow that forces the missing skills.
The 4-Loop Method in One Sentence
Turn each problem into a pattern you can solve and explain under pressure:
Attempt → Review → Redo → Explain
Attempt builds starting ability, review builds defensible correctness, redo builds retrieval, and explain builds interview-ready narration.
One Table: What Each Loop Produces
| Loop | Goal | Timebox | Output you should keep |
|---|---|---|---|
| Attempt | learn to start and commit to an approach | 25–40 minutes | your first solution + where you hesitated |
| Review | turn “works” into “defensible” | 15–30 minutes | edge cases, tests, complexity, one alternative |
| Redo | build retrieval, not recognition | 20–35 minutes | a clean re-implementation without looking |
| Explain | practice interview delivery | 5–10 minutes | a one-minute talk track you can repeat |
Setup: Picking Questions Without Getting Lost
This works with any question source. The key is to pick by pattern, not by mood.
A simple way to stay sane: choose one theme for the day (two pointers, BFS, DP, hashing), do one representative problem, and only add a second problem if it’s a twist on the same theme.
If deciding what to practice drains you, use a bank as a selector and keep your energy for the loops: Interview Questions & Answers Hub.
Loop 1: Attempt (Training “Starting Ability”)
The goal here is not elegance. It’s learning how to start.
Give yourself one rule: no tools during the attempt window. Write something correct, even if it’s brute force, and name your assumptions out loud before you optimize.
A line that saves a lot of panic: “I don’t see the optimal path yet, so I’ll start with a correct baseline and then optimize.”
That sounds like engineering, not weakness.
Loop 2: Review (Where Interview Readiness Gets Built)
“Review” is not just reading a clean solution. It’s where you pressure-test your own thinking.
Keep it small:
- what constraints matter most?
- what inputs break this approach?
- what invariant makes the optimized approach correct?
- what’s the real time/space worst case?
- what’s one alternative, and why not choose it?
Where AI fits in review (without creating dependency)
AI is useful as a challenger after you’ve attempted.
Good prompts are the ones that attack your reasoning:
- “Generate tests that would break my approach and explain why.”
- “Challenge my complexity claim with a worst-case input.”
- “Ask interviewer-style follow-ups that change one constraint.”
Avoid prompts that replace the work (“give me the optimal code”).
If you do use on-screen cues during practice, keep them cue-card small, not sentence-y (the same idea as the teleprompter workflow): Interview Teleprompter.
Loop 3: Redo (The Dependency Killer)
Redo is the moment you find out what you actually learned.
Close everything. Re-implement from memory. Keep the interface the same. Try to write it cleaner.
A simple schedule that works:
- same day: redo for correctness
- two days later: redo for speed
- one week later: redo for interview readiness
If you can’t redo it without looking, you didn’t learn it yet — you recognized it.
Loop 4: Explain (Your “Interview Mode” Loop)
This is the smallest loop and the highest ROI.
Use a one-minute shape: headline approach → invariant → steps → edge cases → complexity → one trade-off sentence
A trade-off sentence that sounds natural: “We use extra memory to avoid quadratic time, which is worth it under these constraints.”
If you want a light structure layer while you practice (especially when nerves kick in), keep it “cue card” small. Many candidates use a one-page set of interview cheat sheets for structure, and only lean on the AI interview assistant to stay oriented—not to generate phrasing.
A Weekly Plan That Doesn’t Burn You Out
You don’t need a heroic schedule. You need a repeatable one.
- 3 days/week: one problem, full 4-loop
- 2 days/week: redo + explain only (older problems)
- 1 day/week: follow-up drills (“what changes if…”) without rewriting everything
If follow-ups are where you spiral, anchor that day to a follow-up pattern playbook: Real-Time Help for Follow-Up Questions. If you want it to feel closer to a real round, run that follow-up day as a timed rep in Beyz practice mode so you’re practicing the loop under mild pressure.
Common Failure Modes
A lot of “the loops aren’t working” feedback is really “the loops are fighting the way human memory works.” Candidate debriefs tend to match the research on retrieval practice and spacing (the “testing effect” and why redoing matters) and what actually counts as effective practice. See: Sage — The Critical Importance of Retrieval for Learning, APS — Effective learning techniques overview.
“I’m doing the loops but I’m too slow.”
Usually they’re spending “new problem time” where they actually need “redo + explain time.” Speed comes after the pattern feels predictable. The quickest fix is boring: do fewer new problems, keep one representative pattern, and repeat the redo until the first 60 seconds (starting) stops feeling like a cliff.
“I keep forgetting after a few days.”
That’s not a discipline issue — it’s a scheduling issue. Most people only practice recognition (“I remember this solution when I see it”). The fix is to bake in one delayed redo slot, even if it’s short. One clean re-implementation two days later beats three fresh problems you’ll forget.
“AI is making me lazier.”
It usually becomes a problem when AI shows up before an honest attempt. If a tool writes the shape of the solution, you’re training tool-usage, not retrieval. The dependency breaker is consistent: attempt first, use AI later as a challenger (tests, edge cases, interviewer-style probes), then close it and redo from memory. If you want a question selector that reduces decision fatigue without turning into “random grinding,” some candidates pair the workflow with IQB interview question bank — but keep the actual learning inside the redo/explain loops.
A candidate debrief: what changed when practice stopped feeling like “grinding”
A common story from candidates is that they did a lot of problems and still froze in mock interviews — not because they couldn’t code, but because the interview starts from blank. One person described the same pattern happening over and over: they could finish a problem after warming up, but the first minute was panic, and follow-ups (edge cases, complexity, “what if constraints change?”) felt like surprise attacks.
What changed wasn’t “doing more.” It was shrinking the scope and finishing the loop. They picked one pattern for a week, used a small set of prompts from IQB to avoid random selection, and treated each problem like something they’d have to defend out loud. The attempt stayed tool-free. The review became adversarial (“what breaks this?”). The redo happened after a gap. And the explain loop became non-negotiable: one minute, in their normal voice, including a trade-off sentence.
In the next mock, when a follow-up arrived (“What if the input is streaming?” / “What’s the worst case?”), they didn’t try to sound polished. They clarified the constraint shift, committed to one angle, and explained the trade-off instead of scrambling. That’s also why pairing this workflow with a follow-up structure can feel natural.
If they used a tool during practice, it stayed “cue card” level — closer to the on-screen cue habits in Interview Teleprompter:How to Use On-Screen Prompts than anything that tries to generate a voice. And once a week, they practiced the whole thing in a more realistic setup so the workflow survived pressure.
Start Practicing Smarter
If you want coding interview practice that transfers, stop at “solved” less often and finish the loop more often. Attempt first, review for break tests and worst cases, redo from memory after a delay, and explain out loud so your reasoning stays steady under pressure. For the broader real-time workflow context, anchor in the pillar guide: Real-Time Interview Assistants: Workflows & Best Practices.
References
- How People Learn II: Learners, Contexts, and Cultures (National Academies Press)
- Optimizing Distributed Practice: Theoretical Analysis and Practical Implications (Cepeda et al., 2009)
- Retrieval-Based Learning: A Decade of Progress (Karpicke, 2017)
- A Proposed Integration of the Expert Performance and Deliberate Practice Perspectives (Kaufman, 2014)
Frequently Asked Questions
What is the best coding interview practice method?
A reliable method is one that forces both problem-solving and communication. The 4-loop method does that by separating the work into attempt, review, redo from memory, and explanation practice. Attempt builds starting ability, review builds correctness and edge-case thinking, redo builds retrieval strength, and explain builds interview-ready narration. The method is simple enough to repeat daily, and strict enough to prevent random grinding.
How many LeetCode problems should I do when practicing for interviews?
Fewer than you think, if your review loop is real. You get more interview value from fully mastering a smaller set of patterns than from sampling many new problems. A good target is one to two problems per day when you do a full attempt-review-redo-explain cycle. Increase volume only when your redo and explanation quality stays high.
How do I practice coding interviews without becoming dependent on AI?
Use a hard rule: attempt first, then use AI only for verification, tests, edge cases, and follow-up questions. Never copy a solution you cannot re-implement. The dependency breaker is the redo loop: close the tool, rewrite from memory, and narrate the approach out loud. Over time, shrink prompts into cue words and rely on repetition rather than generated answers.
How do I practice explaining my code in interviews?
Practice explanation as its own loop. Use a short structure: headline approach, invariant, steps, edge cases, and complexity. Record a one-minute explanation and redo it until it sounds like your normal voice. Interviewers grade reasoning and trade-offs, so narration practice is not extra—it is part of the skill.
What should I do if I keep forgetting solutions after I solve them?
That usually means you learned recognition rather than retrieval. Add spacing and redo from memory after a delay: same day, two days later, and a week later. Focus on the pattern and invariant, not the exact code. If you can re-implement and explain the idea without looking, you actually learned it.