Are you preparing for a coding interview? Then you have probably heard multiple suggestions along the lines of “Just do more LeetCode.” This is consistent advice that has led countless candidates to grind endlessly, solving random problems, encountering hundreds of challenges in the process.

Yet, time and again, sitting in the interview room, many find themselves staring at a seemingly novel problem with blank minds. Despite intense studying and practice, why does this happen, though? The interview environment and structure are a harsh reality that cannot be crossed with just problem-solving skills. You need to develop a deep and structured understanding. So, the problem is not insufficient effort; it lies in the strategy.

The secret to an effective learning strategy is moving from a scattered approach to a methodical, pattern-based system and building real, transferable skills. With that said, let’s find out why grinding problems won’t work anymore, and how you can create a smarter, more effective preparation plan.

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The illusion of progress vs. true retention

Imagine opening LeetCode and struggling to solve a random “medium” problem for an hour. When you finally get it right, you will feel productive and experience a surge of dopamine as you have added another number to your solved count. However, this is merely an “illusion of progress.”

Let’s say that two weeks later, you are presented with the same problem to solve; could you do it with the same insight and efficiency? For many learners, the answer is no. Why? Because solving random problems is reactive. You are simply attempting to tackle new variations without solidifying the foundational patterns. While you remember the solution to a specific problem, you might not have a solid understanding of the underlying principle that would enable you to solve a different question of the same type.

Therefore, your true progress isn’t determined by the number of problems solved, but by the number of algorithms you can reliably recognize and implement under pressure.

Why spaced repetition is non-negotiable

In the 1880s, psychologist Hermann Ebbinghaus identified the “forgetting curve.” It is a model that illustrates how information is lost over time when there is no attempt to retain it. This means that our brain is designed to forget information it considers non-essential. Likewise, when you solve a problem and never revisit it, your brain treats this information as one-time use.

This is why the technique of spaced repetition for coding is essential for aspiring coders. Spaced repetition is basically a learning technique where you review information at increasing intervals to resist the forgetting curve. For example, rather than solving 100 different problems, it’s far more effective to solve 50 fundamental questions multiple times. This approach enables you to revisit core coding patterns, allowing you to move that knowledge from short-term to long-term memory. This is how you turn something into an ingrained skill.

Building on patterns, not just solving problems

The most efficient way to learn is to build knowledge successively. You cannot grasp broader concepts before you understand the basics. For example, if you want to become proficient in solving a complex “Sliding Window” problem, you have to master the simpler “Two Pointers” technique beforehand.

Adopt a pattern-based approach that groups problems by their underlying algorithmic strategy. Such as:

  • Foundation: Two Pointers (for sorted array problems)
  • Builds on: Sliding Window (for subarray/substring problems)
  • Further application: Fast & Slow Pointers (for cycle detection in linked lists)

This interconnected way of learning helps you memorize code and also builds a mental framework. So, whenever you are presented with a unique problem, you can immediately ask yourself, “Which pattern or combination of patterns applies here?” instead of assuming. The ability of systematic deconstruction is what many interviewers look for.

The value of a comprehensive curriculum

While the landscape of coding interview questions is broad, it’s not infinite. The number of core algorithmic patterns that are typically asked in interviews is actually manageable. Most rounds usually cover 15-20 problems, including Binary Search, Depth-First Search, Breadth-First Search, Dynamic Programming, and the aforementioned Two Pointers/Sliding Window, among others.

That is why you need a high-quality curriculum for comprehensive learning. It acts as your structured algorithm path, guaranteeing no knowledge gaps. Without a proper study plan, you will only solve random problems, inadvertently avoiding topics you find difficult, like Graph Theory or Dynamic Programming, leaving you dangerously exposed and lacking in an interview. However, a comprehensive curriculum forces you to confront and learn every core pattern, building a well-rounded skill set.

Building a personalized study plan that actually works

So, what is the alternative solution to grinding? It’s a deliberate, personalized, and consistent coding interview study plan that guides your daily efforts. Here is a blueprint to get you started:

  • Diagnose: Conduct a baseline self-assessment to identify your strengths and weak areas for improvement.
  • Structure: Adopt or create a personalized curriculum that covers all major coding patterns. Consistently dedicate weeks to specific topics until you gain mastery.
  • Learn, don’t just solve: To master each pattern, start by understanding the theory and mechanics. Next, proceed to solve problems, 2-5 classic ones, to internalize the pattern.
  • Implement spaced repetition: If you find some problems too challenging, leverage tools or a simple spreadsheet to track them, and schedule consecutive revisions of these problems over the coming days and weeks.
  • Integrate and simulate: When you feel like you have gained fluency in most patterns and covered related problems, switch to mixed practice. Solve problems from different categories to practice pattern recognition. Finally, practice using mock tests, including whiteboarding, talking through your reasoning, and timing yourself.

Takeaway

Your preparation for a tech interview doesn’t have to cover every single problem. Your goal is to build a strong mental image of coding patterns and the skill to know which tool to use. To achieve this, you need to abandon the random grind and adopt a structured algorithm path. This will build deeper understanding, achieve true retention, and give you the confidence to walk into an interview and tackle whatever comes your way.