How to Solve LeetCode Problems: A Step-by-Step Framework (2026)

Why Most People Practice LeetCode Wrong

The most common mistake is treating LeetCode as a trivia game — seeing the problem, not knowing it immediately, and looking up the answer. This builds exactly the wrong skill. Real interviews don't reward memorization; they reward the ability to work through an unfamiliar problem systematically. The second most common mistake is practicing without a process: jumping between brute force and optimization, losing track of constraints, writing code before understanding the problem. A systematic approach fixes both. Before worrying about how many problems to solve, make sure you're solving each one correctly.

The 6-Step Framework

Apply these steps in order for every problem — not just the hard ones. Skipping steps on easy problems builds bad habits that surface on the medium and hard problems that actually appear in coding interviews.

Step 1: Read Twice — Extract Examples and Edge Cases

Read the entire problem statement twice before touching the keyboard. On the first read, understand what's being asked. On the second read, extract the given examples and identify unstated edge cases:

• What happens with an empty input? A single-element array?
• Are there negative numbers? Duplicate values? Zeros?
• Is the input sorted or unsorted?
• Are indices 0-based or 1-based in the examples?

Write your edge cases down before moving on. In a real interview, asking clarifying questions here demonstrates seniority and prevents wasted time on the wrong problem.

Step 2: Nail the Constraints

Constraints tell you what complexity is acceptable — and therefore what algorithms are in scope. Memorize this table:

• n ≤ 20: O(2ⁿ) or O(n!) acceptable — backtracking, permutations
• n ≤ 100: O(n³) acceptable — triple-nested loops fine
• n ≤ 10,000: O(n²) acceptable — two nested loops fine
• n ≤ 100,000: O(n log n) required — sorting + binary search, heap
• n ≤ 10,000,000: O(n) required — linear scan, two pointers, hashing

Reading constraints first rules out entire families of approaches before you think about them. If n ≤ 10^5, you already know a O(n²) nested loop won't pass — redirect your thinking to O(n log n) algorithms immediately.

Step 3: State the Brute Force First

Before optimizing, verbalize the naive solution: "I can solve this in O(n²) by checking every pair...". Two reasons this matters:

• It confirms you understand the problem correctly
• It gives you a working baseline to compare your optimized solution against

In a real interview, saying "the brute force would be X, but I'm going to optimize to Y" shows structured thinking. Never code the brute force unless asked — just state it and move on. The exception: if you're stuck and can't find the optimal approach after 10 minutes, implement the brute force to get partial credit and demonstrate you can write working code.

Step 4: Identify the Pattern

This is where most candidates need the most practice. Before writing code, name the pattern you're going to use. The 8 patterns that cover ~80% of FAANG interview problems:

• Two pointers: Pair sum, container with most water, 3-sum
• Sliding window: Maximum subarray, longest substring without repeating
• Binary search: Sorted rotated array, search range, binary search on answer
• BFS/DFS: Islands, shortest path, connected components, word ladder
• Tree DFS/BFS: Path sums, LCA, level-order traversal
• Dynamic programming: Knapsack, coin change, edit distance, palindromes
• Monotonic stack: Next greater element, daily temperatures, largest rectangle
• Heap / priority queue: Top-K elements, merge K sorted lists, median finder

If you can't identify a pattern after 5 minutes, re-read the constraints. The constraint range almost always hints at the expected complexity, which narrows the pattern. Pair this with our guide on approaching DSA problems for more pattern identification techniques.

Step 5: Code Cleanly

Once you know the pattern and approach, write the code with these habits:

• Meaningful variable names: left and right, not i and j for two-pointer indices
• Handle edge cases first: Empty input, null checks, single-element arrays — add these before the main logic
• Comment your key decisions: A one-line comment when you set up a hash map or a window is enough to signal understanding
• No shortcuts on variable names under pressure: Single-letter variables cause bugs and confuse interviewers

In an interview context, think out loud as you code. Say "I'm initializing a left pointer at 0 and expanding the window to the right...". This lets the interviewer understand your reasoning and offer hints if you veer off track.

Step 6: Optimize and Verify

After writing the solution, do three things before declaring done:

• Trace through the example: Manually run through the provided test case line by line. Catch off-by-one errors before running.
• State the complexity: "This is O(n) time and O(1) space because...". You should be able to justify both.
• Check your edge cases: Return to the list from Step 1. Does your solution handle the empty input? The single-element case? Negative numbers?

If the interviewer asks "can you optimize further?", that's not a failure signal — it's an invitation to discuss trade-offs. The answer is often: "We could reduce space from O(n) to O(1) by using two pointers, at the cost of modifying the input array."

How to Review Problems You Couldn't Solve

When you can't solve a problem after 25–35 minutes, the right process is:

• Day 0 (now): Read the solution approach (not the code). Identify which pattern you missed and why. Don't just copy the code.
• Day 0 (30 min later): Implement the solution from memory without referring to the written code. Getting stuck here is okay — it means you're actually learning.
• Day 2–3: Return to the same problem. Try solving it again from scratch without any notes. This is where retention is built.
• Day 7: Final review. If you can solve it cleanly, mark it done. If not, it goes back into rotation.

Building Pattern Recognition Over Time

After solving 30–40 problems with this framework, pattern recognition becomes semi-automatic. To accelerate it: after every 10 problems, review which patterns you used. If you've done 10 tree problems and all were DFS, add 5 BFS tree problems. If you've never used a monotonic stack, do a dedicated week on it. Track your weaknesses explicitly — most people avoid the patterns they find hardest, which is the opposite of what builds interview readiness. When you feel ready, validate against company-specific problem patterns using our LeetCode interview questions by company guide. Check also how many problems to target for your specific company tier.