Summary of "How to Solve ANY LeetCode Problem (Step-by-Step)"

Main ideas / lessons

• You don’t need to solve hundreds of LeetCode problems to do well in interviews. Instead, use a systematic, repeatable approach.
• Any LeetCode/interview problem can be reduced to:
  • Inputs → transform them → expected outputs, even if the prompt looks complicated.
• Before coding, clarify the problem:
  • Restate the problem as “inputs are…, we do…, we get…”
  • Manually work through a test case if stuck
  • Ask clarifying questions in real interviews
  • Explicitly confirm edge cases (interviewers expect you to catch these)
• LeetCode is pattern recognition: most problems map to common data structure + algorithm patterns.
• Use a decision process to determine the correct algorithm:
  • Often solvable by identifying whether it’s a graph, shortest path problem, etc.
• Once the pattern is identified, implement the known approach and practice because solutions often share the same core code structure.
• Debugging comes after implementation:
  • Distinguish syntax errors vs. failing tests
  • For failing tests, start with edge cases (few failures) or manual tracing + logging (many failures)

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Step-by-step methodology (explicit instructions)

1) Simplify the problem (what to do first)

• Identify what the problem gives you (the inputs).
• Identify what transformation/action is required to produce the outputs.
• Restate it in a repeatable format (to yourself or out loud to an interviewer):
  • “The inputs are these…”
  • “We do this to the inputs…”
  • “We get this as the output…”
• If you don’t understand the mapping from inputs to outputs:
  • Manually walk through a test case.
• If this is an interview, ask clarifying questions (bonus marks):
  • Confirm assumptions about guarantees, formatting, and interpretation.
• Pay special attention to edge cases:
  • Cases where behavior may be ambiguous or differs from the norm.

Example applied: Word Ladder

• Simplify
  • Inputs: beginWord, endWord, and wordList
  • Action: change one letter at a time in beginWord to reach words in wordList
  • Output: the minimum number of words/steps from start to end; if impossible output 0
• Walk-through test case
  • hit → hot → dot → dog → cog (reaches cog in 5 words total)
• Sample clarifying questions
  • Is endWord guaranteed to be in the word list?
  • Are words case sensitive?
• Sample edge cases to clarify
  • If beginWord == endWord, should the answer be length 1 or 0?
  • If wordList is empty, should the answer be 0?

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2) Choose an approach by recognizing the pattern (which algorithm to use)

• Ensure you’re familiar with:
  • Big O notation
  • Data structures & algorithms
• Use a structured decision process (the video references a “free flowchart”) to identify the pattern:
  • Is it a graph problem?
    • If you can represent it as nodes/edges or pathfinding → likely graph → YES
  • Is it a tree?
    • If mentions roots/leaves → likely tree → otherwise NO
  • Is it related to directed acyclic graphs (DAG)?
    • If direction is implied → YES; otherwise NO
  • Is it a shortest path problem?
    • If asked for shortest path/least steps → YES
  • Is the graph weighted?
    • If each move has the same cost → unweighted
• Once the pattern is identified, commit to the corresponding algorithm.

Example applied: Word Ladder decision

• Graph? YES (transformations can be modeled as connections between words)
• Tree? NO (no roots/leaves)
• DAG? NO (transformations are reversible; no inherent direction)
• Shortest path? YES (need shortest transformation sequence)
• Weighted? NO (each letter transformation counts as 1)

✅ Therefore: Use BFS.

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3) Implement the chosen pattern (BFS for Word Ladder)

• Use BFS with slight tweaks:
  • Starting node = beginWord
  • Track distance/level (number of transformations/steps so far)
  • Maintain a queue for BFS expansion
  • Maintain a visited set to avoid revisiting words

Core BFS transformation logic (as described)

• While processing each word from the queue:
  • For each position/letter in the word:
    • Replace it with every possible letter (a–z)
    • Consider the new candidate word only if it satisfies all 3 conditions:
      1. It’s not the same as the current word (not an identical transformation)
      2. It exists in wordList
      3. It has not been seen before (not in visited)
• For each valid candidate:
  • If it equals endWord:
    • return currentLevel + 1 (the solution length/steps)
  • Otherwise:
    • add candidate to the queue
    • add it to visited
    • continue BFS (the level increases after each BFS layer)

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4) Debug after running (handling failures)

• Two broad error types:
  • Syntax error (code won’t run)
    • Fix by reading the error log and correcting the syntax
  • Implementation error (tests fail even though code runs)
• Debugging strategy:
  • If only 1–2 tests fail:
    • likely missing an edge case
    • add/adjust logic to handle that edge case
  • If multiple tests fail:
    • likely incorrect implementation
    • Manually walk through your code on the failing test case, line by line
    • if still unclear:
      • add print/logging statements at different points to inspect intermediate inputs/outputs
      • identify where the behavior diverges from what you expected
• Key encouragement:
  • Finding the approach is easier with practice
  • Debugging is hardest, and practice helps you recognize common mistakes

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5) Finish and submit

• After passing tests:
  • Submit on LeetCode or run with the interviewer
• Confidence message:
  • Following this step-by-step process + understanding data structures/algorithms should help you solve any LeetCode/interview problem

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Sponsors / tools mentioned

• AlgoMonster
  • Provides a FREE flowchart for identifying patterns
  • Mentions paid content: $7/month
  • Mentions discount code: BAGEL for an extra 10% off

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Speakers / sources featured

• Single speaker: an unnamed narrator/presenter (the video’s author/teacher)
• Sponsor/source: AlgoMonster (flowchart and interview-prep content)

Category

Educational

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