Solving problems is the core of being a great programmer, engineer, or thinker. But many people struggle to move from a simple, working solution to an elegant, efficient one. In this blog, we’ll break down the step-by-step process to solve any problem — from scratch to scale.
🪜 Step 1: Understand the Problem — Really Well
Before writing code, get clarity.
- ✅ What are the inputs?
- ✅ What’s the expected output?
- ✅ Are there constraints like time or memory limits?
- ✅ What are the edge cases?
If your understanding is flawed, no optimization will save you.
🧱 Step 2: Start with a Brute Force (Basic) Approach
Begin with the simplest solution you can think of — even if it’s not optimal. This helps you get something working and lays the foundation for optimization.
Examples:
- Loop through everything.
- Use recursion without memoization.
- Try sorting first and then brute search.
“Make it work, first. Then make it better.”
⏱️ Step 3: Analyze Time and Space Complexity
Once the brute-force version works, ask:
- How fast is it? →
O(n),O(n^2), etc. - How much memory does it use?
This step reveals whether your solution will scale.
🔍 Step 4: Find Bottlenecks or Redundant Work
Look for patterns like:
- Are you recalculating the same results repeatedly?
- Can you store past results? (Memoization)
- Can you search faster with hashing or sorting?
- Are you using the right data structure?
⚙️ Step 5: Optimize Strategically (Intermediate to Advanced)
Apply known patterns to reduce time or space:
- Hashing → Fast lookup
- Two Pointers / Sliding Window → Efficient linear scans
- Binary Search / Divide and Conquer → Logarithmic performance
- Dynamic Programming → Break problem into subproblems
- Greedy Algorithms → When local choices lead to global optimum
Choose the strategy that fits the problem type.
🧪 Step 6: Test, Test, Test
Make sure your solution works by testing:
- Regular test cases
- Edge cases (empty input, one element, max/min values)
- Large input sets (to test performance)
Never trust code that hasn’t been tested thoroughly.
🧹 Step 7: Refactor and Clean Up
Once your logic is sound:
- Break large functions into smaller ones
- Rename confusing variables
- Add comments if needed
- Remove dead code
Clean code is easier to maintain, debug, and share.
💡 Think Like a System Engineer (Advanced Layer)
When working with large-scale systems or real-world applications:
- Can you use caching, concurrency, or load balancing?
- Are you minimizing database/API calls?
- Are your algorithms optimized for latency and throughput?
🧠 Summary: The 7-Step Problem Solving Framework
| Stage | Goal | Tools/Methods |
|---|---|---|
| Understand Problem | Know what you’re solving | Clarify input/output, edge cases |
| Brute Force | Get it working | Naive loops, recursion |
| Analyze Complexity | Spot inefficiencies | Big O analysis |
| Optimize | Make it faster and scalable | Hashing, DP, Binary Search, Greedy, etc. |
| Test | Confirm reliability | Edge cases, stress test |
| Refactor | Make it clean | Code style, comments, modular design |
| Think Systemically | Handle real-world scale | Caching, APIs, parallelism, memory tuning |
✨ Final Thought
“First make it work, then make it right, then make it fast.“
— Kent Beck, Creator of Extreme Programming
Solving problems efficiently is a learnable skill. The more you practice with intention, the more naturally these steps will come to you.
Want to apply this to a real problem? Drop one in the comments or message me — let’s break it down together.
