How to Solve LeetCode Problems: A Step-by-Step Framework

Most people fail at LeetCode not because they lack intelligence or knowledge — they fail because they lack a systematic process. They read a problem, stare at the screen for ten minutes, then jump straight to code without a plan. When the code doesn't work, they start over.

The result is a frustrating cycle: attempt a problem, get stuck, look at the solution, feel like you understand it, move on — and then get stuck on the next problem in exactly the same way. This happens because reading a solution is not the same as learning how to find one.

The fix is straightforward. You need a repeatable framework that breaks problem-solving into discrete steps. Each step has a clear input, a clear output, and a clear decision point. Once you internalize this framework, you'll approach every problem the same way — and your hit rate will climb steadily.

This guide gives you that framework. It works for easy, medium, and hard problems across every category — arrays, trees, graphs, dynamic programming, and everything in between.

Every LeetCode problem, regardless of difficulty, can be approached with the same five steps. The steps are sequential — skipping one almost always costs you more time than it saves.

This framework mirrors how experienced engineers solve problems in interviews. Interviewers don't just want correct code — they want to see structured thinking. Following these steps makes your thought process visible.

Step 1 is where most people cut corners — and where the best solvers gain their advantage. Reading a LeetCode problem is not like reading an email. You need to extract specific information systematically.

Start by identifying the exact input and output types. Is the input an array of integers, a string, a linked list node, or a tree root? What exactly should you return — a number, a boolean, a modified data structure, or a list of results?

Next, read the constraints section carefully. Constraints tell you which approaches are viable. If n <= 10, brute force works. If n <= 10^4, O(n^2) might pass. If n <= 10^5, you need O(n log n) or better. If n <= 10^6, you almost certainly need O(n).

Finally, work through the examples by hand. Don't just read them — trace through the logic step by step. This is where you catch misunderstandings before they cost you twenty minutes of debugging.

Step 2 is the bridge between understanding a problem and knowing how to solve it. Pattern recognition is the single most important skill in LeetCode — and the good news is that it's a learnable skill, not an innate talent.

There are roughly 15 core patterns that cover the vast majority of interview problems. When you read a problem, your job is to identify which pattern fits. The clues are in the problem structure, the constraints, and the expected output.

For example, if a problem asks about contiguous subarrays of variable length, think sliding window. If it involves a sorted array and finding pairs, think two pointers. If it asks for all possible combinations or permutations, think backtracking. If a problem has overlapping subproblems with optimal substructure, think dynamic programming.

You don't need to memorize every problem. You need to memorize the patterns and the signals that trigger them. YeetCode's flashcard system is specifically designed for this — drilling pattern recognition rather than solution memorization.

Steps 3 and 4 go together. Once you've identified the pattern, write pseudocode before real code. Pseudocode forces you to think about the algorithm's structure without getting bogged down in syntax.

Your pseudocode should answer three questions: What data structure will I use? What's my iteration strategy (loop, recursion, BFS queue)? What's my termination condition?

When translating to code, start with the skeleton — function signature, main loop, return statement. Then fill in the logic step by step. Don't try to write the entire solution in one pass. Build it incrementally and test mentally at each stage.

Name your variables clearly. Use "left" and "right" for two pointers, "window_sum" for sliding window accumulators, "visited" for graph traversal sets. Clear naming prevents bugs and makes your code readable to interviewers.

Step 5 separates good solvers from great ones. After your code passes the basic examples, don't immediately submit. First, test it against edge cases: empty input, single element, maximum constraint values, all-same elements.

If your solution fails, resist the urge to randomly tweak code. Instead, add print statements (or trace mentally) to find where the actual output diverges from the expected output. The bug is almost always in your loop bounds, your comparison operators, or your initialization.

Once your solution is correct, consider optimization. Can you reduce time complexity? Common upgrades include replacing nested loops with a hash map (O(n^2) to O(n)), using binary search instead of linear scan (O(n) to O(log n)), or adding memoization to recursive solutions.

In an interview, always state your current time and space complexity out loud before optimizing. This shows the interviewer you understand the tradeoffs, even if you don't have time to implement the optimal solution.

A framework is only useful if you internalize it through practice. The goal is not to consciously run through five steps forever — it's to make this process automatic so you can focus on the hard parts of each problem.

Start with easy problems to build confidence with the framework. Move to mediums once you can solve most easies within 15 minutes. Only tackle hards when you're consistently solving mediums in under 25 minutes.

Spaced repetition is your secret weapon. Re-solving a problem you solved two weeks ago takes 5 minutes but reinforces the pattern connection in your memory. Without review, you'll forget 80% of what you learned within a month.

Track your progress by category, not just by count. Saying "I've done 200 problems" means nothing if 150 of them were arrays. Make sure you have at least 5-10 problems solved in each major category: arrays, strings, linked lists, trees, graphs, dynamic programming, and design.

The framework in this guide works because it mirrors how experienced engineers actually think. With consistent practice — even just one problem per day — you'll find that problems that once seemed impossible start to feel like variations of patterns you already know.