Originally published on LeetCopilot Blog
A structured, week-by-week study plan that leverages AI tools to take you from complete beginner to FAANG interview-ready. Includes specific prompts, problem recommendations, and realistic timelines.
You want to work at a top tech company. The path goes through LeetCode. But staring at 2000+ problems with no roadmap is overwhelming.
Traditional study plans tell you what to practice. This plan tells you how to practice—using AI tools to accelerate each phase of your learning journey. Whether you have 6 weeks or 6 months, this AI-powered approach will get you interview-ready faster than grinding alone.
This is the study plan I wish I had when I started. It's built for complete beginners but scales to FAANG-level preparation.
TL;DR
- Who this is for: Complete beginners, career switchers, or anyone stuck on LeetCode despite months of practice.
- The approach: AI-assisted learning that uses AI for understanding (not cheating), combined with intentional re-solving for retention.
- Timeline options: 6 weeks (intensive), 12 weeks (balanced), or 6 months (part-time).
- Key principle: AI reduces wasted time, not total practice time. You'll still solve hundreds of problems—just more efficiently.
- Output: Pattern recognition, problem-solving intuition, and genuine interview readiness.
Who This Plan Is For
Complete Beginners
You know basic programming (loops, conditionals, functions) but have never touched competitive coding or algorithm problems. Terms like "dynamic programming" or "sliding window" are unfamiliar.
What you need: Foundational pattern exposure, vocabulary building, and guided problem selection.
Non-CS Background
You're a self-taught developer, bootcamp grad, or career switcher. You can build applications but lack formal CS training in data structures and algorithms.
What you need: Structured DSA learning integrated with practice problems.
People Stuck on LeetCode
You've solved 50-100 problems but hit a plateau. You can solve Easy problems but struggle with Medium. New problems still feel completely new.
What you need: Pattern recognition training and deliberate practice on weak areas.
Week-by-Week AI-Assisted Learning Plan
This plan assumes 1-2 hours daily. Adjust timing proportionally for more or less availability.
Week 1: Patterns & Foundations
Goal: Understand what patterns are and why they matter.
Day 1-2: Introduction to Patterns
Solve these problems:
- Two Sum (Easy)
- Contains Duplicate (Easy)
- Valid Anagram (Easy)
AI usage:
After each problem, ask:
"What pattern does this problem use?
Why does that pattern apply here?
What other problems use the same pattern?"
Day 3-4: Array Manipulation
Solve:
- Best Time to Buy and Sell Stock (Easy)
- Maximum Subarray (Medium)
- Product of Array Except Self (Medium)
AI usage:
Before solving:
"Break down this problem into subproblems.
What's the brute force approach?
What's the key insight that optimizes it?"
Day 5-7: Hash Maps Deep Dive
Solve:
- Group Anagrams (Medium)
- Top K Frequent Elements (Medium)
- Longest Consecutive Sequence (Medium)
AI usage:
"When should I use a hash map vs. sorting?
What's the time-space tradeoff here?"
Week 1 Checkpoint: You should recognize when hash maps enable O(n) solutions and understand the Two Sum pattern family.
Week 2: Array + Sliding Window Using AI Prompts
Goal: Master the sliding window pattern—one of the highest-ROI patterns for interviews.
Day 1-2: Fixed Window
Solve:
- Maximum Average Subarray I (Easy)
- Find All Anagrams in a String (Medium)
AI prompts:
"Walk me through the sliding window on this example step by step.
Show me the window contents and sum at each position."
Day 3-4: Variable Window
Solve:
- Longest Substring Without Repeating Characters (Medium)
- Minimum Window Substring (Hard)
AI prompts:
"What determines when to expand vs. shrink the window?
What invariant am I maintaining as the window moves?"
For deeper sliding window study, see our sliding window roadmap.
Day 5-7: Two Pointers
Solve:
- 3Sum (Medium)
- Container With Most Water (Medium)
- Trapping Rain Water (Hard)
AI prompts:
"When do I use two pointers vs. sliding window?
What properties of the input make two pointers work here?"
Week 2 Checkpoint: You should be able to identify sliding window problems and implement both fixed and variable window approaches.
Week 3: Recursion + Backtracking with AI Helpers
Goal: Build comfort with recursive thinking and backtracking templates.
Day 1-2: Recursion Basics
Solve:
- Climbing Stairs (Easy)
- Fibonacci Number (Easy)
- Power of Two (Easy)
AI prompts:
"Draw the recursion tree for n=5.
Where are the overlapping subproblems?
How does memoization help here?"
Day 3-4: Binary Tree Recursion
Solve:
- Maximum Depth of Binary Tree (Easy)
- Invert Binary Tree (Easy)
- Validate Binary Search Tree (Medium)
AI prompts:
"What's the recurrence relation for this tree problem?
At each node, what information do I need from children?"
Day 5-7: Backtracking
Solve:
- Subsets (Medium)
- Permutations (Medium)
- Combination Sum (Medium)
AI prompts:
"Show me the decision tree for this backtracking problem.
What are my choices at each level?
When do I add to result vs. backtrack?"
Week 3 Checkpoint: You should understand recursion mechanics, tree traversal patterns, and the backtracking template.
Week 4: Systematic DP Learning with AI
Goal: Demystify dynamic programming—the pattern that intimidates most candidates.
Day 1-2: 1D DP
Solve:
- Climbing Stairs (revisit with DP lens)
- House Robber (Medium)
- Coin Change (Medium)
AI prompts:
"What's the state? What does dp[i] represent?
What's the recurrence relation?
What are the base cases?"
Day 3-4: 2D DP
Solve:
- Unique Paths (Medium)
- Longest Common Subsequence (Medium)
- Edit Distance (Medium)
AI prompts:
"Why does this need 2D DP? What are the two dimensions?
Walk me through filling the DP table for a small example."
Day 5-7: DP Patterns
Solve:
- Longest Increasing Subsequence (Medium)
- Word Break (Medium)
- Partition Equal Subset Sum (Medium)
AI prompts:
"What DP pattern is this? (0/1 Knapsack? LCS? LIS?)
How do I recognize this pattern in new problems?"
Week 4 Checkpoint: You should be able to formulate DP state, recurrence, and base cases for standard problem types.
AI Tools That Work Best for Each Stage
Visualization Tools
Best for: Understanding recursion trees, DP table filling, sliding window movement.
Use AI to generate step-by-step visualizations:
"Visualize the recursion tree for fib(5)"
"Show me the DP table for LCS of 'ABCDE' and 'ACE'"
"Trace the sliding window through [1,2,3,1,1,1,2,3]"
Step-by-Step Breakdown Engines
Best for: Understanding solutions you've read.
"I read this solution but don't understand line 15.
Walk me through what happens at this specific line."
Code Critique Tools
Best for: Improving your working solutions.
"Here's my solution that works.
Is there a more efficient approach?
Are there edge cases I'm missing?"
LeetCopilot
LeetCopilot is specifically designed for this workflow:
- Progressive hints: Get nudged toward the solution without spoilers
- Problem context: Knows which problem you're solving
- Learning focus: Helps you understand, not just complete
- Integrated: Works directly on LeetCode
Best for: The "stuck but want to solve it myself" scenario that's most effective for learning.
Realistic Timeline to Become Interview-Ready
6-Week Intensive Plan
Requirements: 3-4 hours daily, 7 days/week
| Week | Focus | Problems |
|---|---|---|
| 1 | Arrays, Hash Maps | 20 |
| 2 | Sliding Window, Two Pointers | 20 |
| 3 | Recursion, Trees, Backtracking | 25 |
| 4 | Dynamic Programming | 20 |
| 5 | Graphs, BFS/DFS | 20 |
| 6 | Mock Interviews, Review | 15 |
Total: ~120 problems + interview practice
Best for: Career changers with time off, people with imminent interview deadlines.
12-Week Balanced Plan
Requirements: 1.5-2 hours daily, 6 days/week
| Weeks | Focus | Problems |
|---|---|---|
| 1-2 | Arrays, Hash Maps | 25 |
| 3-4 | Sliding Window, Two Pointers | 25 |
| 5-6 | Recursion, Trees | 25 |
| 7-8 | Backtracking, DP Intro | 25 |
| 9-10 | DP Mastery, Graphs | 25 |
| 11-12 | Mixed Review, Mock Interviews | 25 |
Total: ~150 problems over 3 months
Best for: Working professionals balancing job and prep.
6-Month Part-Time Plan
Requirements: 1 hour daily, 5 days/week
| Months | Focus |
|---|---|
| 1 | Arrays, Strings, Hash Maps |
| 2 | Two Pointers, Sliding Window |
| 3 | Recursion, Binary Trees |
| 4 | Backtracking, Intro DP |
| 5 | Advanced DP, Graphs |
| 6 | System Design Intro, Mock Interviews |
Total: ~200 problems over 6 months
Best for: Long-term career planning, minimal daily commitment.
Common Pitfalls in AI-Assisted Study
Pitfall 1: Over-Relying on Explanations
The trap: You read AI explanations and think you understand. But understanding is not the same as doing.
The fix: Every problem you solve with AI help must be re-solved without help the next day. Track your re-solve rate—it should be 80%+.
Pitfall 2: Not Practicing Under Time Pressure
The trap: AI-assisted practice has no clock. Real interviews do. You build understanding but not speed.
The fix: Dedicate 20% of practice to timed sessions without AI. Build the pressure tolerance that interviews demand.
Pitfall 3: Asking for Full Solutions
The trap: "Just give me the answer" feels efficient but destroys learning. You build recognition, not generation ability.
The fix: Commit to hints-only AI usage. Set a rule: never ask AI for complete solutions.
Pitfall 4: Skipping Easy Problems
The trap: "Easy problems are beneath me." But Easy problems build pattern fluency that makes Medium problems easier.
The fix: Start each pattern with 3-5 Easy problems before attempting Medium. This isn't wasted time—it's foundation building.
Pitfall 5: Random Problem Selection
The trap: Solving random problems feels productive but builds scattered knowledge.
The fix: Follow a structured plan (like this one). Study patterns in sequence, not randomly.
FAQ
How do I know when I'm ready for interviews?
When you can solve 70%+ of Medium problems in 25 minutes without AI help. Mock interviews (on platforms like Pramp) give the most realistic signal.
What if I can't afford 6 weeks full-time?
Use the 12-week or 6-month plans. Consistency beats intensity. One hour daily for 6 months is effective.
Should I use AI for every problem?
No. As you progress, reduce AI reliance. By week 4+, you should only need AI for Hard problems or unfamiliar patterns.
Can I skip patterns I find boring?
You can, but it's risky. Interview questions are unpredictable. Cover all core patterns at least once.
Is this plan only for FAANG?
No. This approach works for any company with algorithmic interviews. FAANG-level prep is sufficient for most tech companies.
Conclusion
The path from beginner to FAANG-ready is challenging but well-defined. AI tools make the journey faster, not easier—you still need to put in the work.
This plan gives you structure: which patterns to learn, in which order, with which AI techniques. Follow it consistently, and you'll build the pattern recognition and problem-solving skills that interviewers are looking for.
Remember the core principles:
- AI is for understanding, not shortcuts
- Re-solve every AI-assisted problem without help
- Practice under time pressure regularly
- Trust the structured approach over random grinding
The tools are available. The roadmap is clear. The only variable is your commitment. Start this week, stay consistent, and watch your LeetCode skills transform.
You're not just preparing for interviews—you're building the problem-solving ability that will serve your entire career. That journey starts now.
If you're looking for an AI assistant to help you master LeetCode patterns and prepare for coding interviews, check out LeetCopilot.
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