The Writing Habit That Improved My Programming Skills

I used to think writing and coding were completely separate skills.

Writing was for English majors and marketing people. Coding was for logical thinkers who preferred precise syntax over messy human language. I spent my first few years as a developer actively avoiding any writing that wasn't absolutely necessary—terse commit messages, minimal documentation, code comments only when forced.

Then I started a programming blog.

Not because I thought I had profound insights to share with the world, but because I kept forgetting solutions to problems I'd solved weeks earlier. The blog was meant to be my external memory—a place to document fixes and approaches I might need again.

What I discovered completely changed how I approach software development. The act of writing about code didn't just help me remember solutions. It made me a fundamentally better programmer.

Not because writing taught me new technical skills, but because it forced me to think more clearly about the problems I was solving and the solutions I was building.

The Explanation Gap

The first time I tried to write about a coding problem I'd solved, I hit a wall immediately.

I'd spent three hours debugging a React component that wasn't re-rendering properly. I'd finally fixed it by moving a useEffect dependency, and everything worked perfectly. Seemed like a straightforward blog post: "How to fix React re-rendering issues."

But when I sat down to explain what I'd done and why it worked, I realized I didn't actually understand my own solution.

I could describe the steps I'd taken, but I couldn't explain the underlying reason the fix worked. I knew that moving the dependency solved the problem, but not why. I'd gotten lucky with a solution that worked without truly grasping the mechanics behind it.

That's when I discovered the explanation gap—the difference between knowing how to do something and knowing why it works.

Writing forces you to bridge that gap. You can't explain something clearly to others if you don't understand it clearly yourself. The act of trying to articulate a solution reveals all the holes in your understanding.

The Mental Model Clarification

Once I started writing regularly about programming concepts, my debugging skills improved dramatically.

Not because I was learning more syntax or memorizing more APIs, but because I was developing clearer mental models of how systems actually work. When you have to explain how JavaScript's event loop handles asynchronous operations, you can't just wave your hands and say "it's complicated." You have to trace through the actual process step by step.

This precision of understanding translated directly to better problem-solving. When something broke, instead of trying random fixes until something worked, I could reason through what might be causing the issue based on my understanding of the underlying mechanisms.

I started using Crompt's AI tutor to help me prepare explanations for complex topics. Before writing a post about database indexing or API design patterns, I'd discuss the concept with the AI first. This helped me identify gaps in my understanding and find clearer ways to explain difficult concepts.

The AI became like a rubber duck debugging partner—helping me think through ideas before committing them to writing.

The Abstraction Practice

Writing about code taught me something crucial about software architecture: the ability to work at different levels of abstraction.

When you're deep in implementation details, it's easy to lose sight of the bigger picture. But when you have to write about what you're building, you're forced to step back and describe not just the how but the what and why.

This practice of moving between concrete implementation and abstract concepts made me better at designing systems. I started thinking more about interfaces and contracts, not just the specific functions I was writing. I got better at identifying when I was solving the wrong problem or building unnecessarily complex solutions.

Last month, I was working on a data processing pipeline that was getting unwieldy. Instead of continuing to add features, I took a break to write about the architecture challenges I was facing. The writing process helped me realize I was trying to solve three different problems with one system. That insight led to a much cleaner design that separated concerns properly.

The Assumption Surfacing

One of the most valuable aspects of writing about programming is how it surfaces hidden assumptions.

When you're coding, you make hundreds of small assumptions about how users will behave, what data will look like, how systems will interact. Most of these assumptions stay implicit—buried in code comments or just floating around in your head.

Writing forces you to make assumptions explicit. When you try to explain why you chose one approach over another, you have to articulate the trade-offs and constraints you were considering. Often, that's when you discover your assumptions were wrong or incomplete.

I was writing about API design patterns when I realized I'd been making a huge assumption about how clients would use one of our endpoints. I assumed they'd always paginate through results sequentially, so I optimized for that use case. But when I tried to document the API, I realized many clients would want to jump to specific pages or sort results differently.

That documentation process led to a much more flexible API design that served real use cases instead of my assumptions about use cases.

The Learning Acceleration

Writing about programming concepts accelerated my learning in ways I never expected.

When you read documentation or tutorials, you're consuming information passively. When you write about the same concepts, you're actively processing and reorganizing that information. The difference is profound.

I started a habit of writing a short post every time I learned something new, even if it was just a small technique or useful command. These posts weren't comprehensive tutorials—just 300-500 word explanations of concepts I'd recently grasped.

The act of writing these mini-explanations helped concepts stick in ways that just reading about them never did. More importantly, it helped me connect new information to things I already knew. Writing became a way of integrating new knowledge into my existing mental framework.

I use Crompt's document summarizer when I'm researching complex technical topics. Instead of trying to read entire papers or documentation sets, I can upload key documents and get focused summaries that highlight the main concepts I need to understand. This gives me a foundation to build on when writing my own explanations.

The Code Review Translation

Writing about programming dramatically improved my code review skills.

When you practice explaining code concepts clearly, you get better at reading other people's code and understanding their reasoning. You also get better at giving feedback that's constructive rather than just critical.

Instead of leaving review comments like "this is confusing" or "use a different approach," I started writing comments that explained what I found confusing and suggested specific improvements. This made my reviews more helpful to teammates and led to better technical discussions.

The writing practice also made me more thoughtful about my own code. When you know you might have to explain your implementation decisions later, you naturally write more readable code and make more intentional choices about architecture and naming.

The Problem Decomposition Skill

Perhaps the most valuable programming skill writing taught me was problem decomposition.

Every blog post requires breaking down complex topics into digestible pieces. You have to identify the core concepts, figure out the logical order to present them, and find ways to make difficult ideas accessible. This is exactly the same skill you need for architecting software systems.

When I approach a new feature now, I often start by writing a brief explanation of what I'm trying to build and why. Not documentation for others—just a way to clarify my own thinking about the problem space.

This writing-first approach helps me identify edge cases I might miss, think through error handling scenarios, and consider how the feature fits into the larger system. It prevents me from jumping straight into implementation before I fully understand what I'm trying to solve.

The Communication Multiplier

The technical writing habit didn't just make me a better programmer—it made me a more effective team member.

When you can explain complex technical concepts clearly, you become the person others come to for help understanding difficult problems. You can bridge the gap between technical and non-technical stakeholders. You can document decisions in ways that help future team members understand not just what was built, but why it was built that way.

I started keeping a shared team blog where we documented interesting technical challenges and their solutions. This became an invaluable resource for onboarding new developers and resolving similar issues when they came up again.

The writing practice also made me better at technical presentations and architecture discussions. When you're used to explaining concepts in writing, adapting those explanations to verbal communication becomes much easier.

The Debugging Narrative

One unexpected benefit was how writing improved my debugging process.

When I hit a particularly tricky bug, I started writing about it as I worked through the investigation. Not polished blog posts, just stream-of-consciousness documentation of my debugging process. What I tried, what happened, what I learned from each attempt.

This narrative approach helped me see patterns I would have missed otherwise. It also prevented me from repeating dead-end approaches I'd already tried. Most importantly, it helped me think more systematically about debugging instead of just trying random fixes.

I use Crompt's research paper summarizer when I'm investigating complex technical issues that require understanding academic research or detailed specifications. The AI helps me extract the key insights from dense technical papers, giving me more time to focus on applying those insights to my specific debugging challenges.

The Learning in Public Effect

Making my technical writing public created an unexpected feedback loop that accelerated my growth.

When you publish your understanding of programming concepts, people will correct your mistakes and suggest improvements. This can be intimidating at first, but it's incredibly valuable for learning.

I've had senior developers point out flaws in my reasoning, suggest better approaches to problems I thought I'd solved, and recommend resources that deepened my understanding of topics I'd covered superficially.

This public accountability also motivated me to research topics more thoroughly before writing about them. Knowing that other developers might read and critique my explanations pushed me to be more rigorous in my understanding.

The Career Acceleration

The writing habit became one of the most valuable career investments I've made.

Technical writing demonstrated my communication skills to current and potential employers. It showed that I could not only solve problems but also document and share solutions effectively. Several job opportunities came directly from people who had read my blog posts and were impressed with my explanations of complex topics.

But the indirect career benefits were even more valuable. The clearer thinking, better debugging skills, and improved ability to work at different levels of abstraction made me a more effective developer overall. I could tackle more complex problems, contribute to architectural decisions, and help teammates solve difficult issues.

The Simple Practice

The writing habit that improved my programming skills is remarkably simple: after solving any non-trivial technical problem, write a brief explanation of what you learned.

Not a perfect tutorial or comprehensive guide. Just 300-500 words explaining the problem, your approach, and why your solution works. Focus on the concepts and reasoning, not just the specific code.

Do this consistently, and you'll start noticing improvements in how you think about code, approach new problems, and understand complex systems. The writing isn't separate from programming—it's a tool that makes you better at every aspect of software development.

Because programming isn't just about writing code that works. It's about building systems you can understand, explain, and maintain. And that requires the kind of clear thinking that only comes from practice articulating complex ideas simply.

The best programmers aren't just good at solving problems—they're good at understanding and explaining their solutions. Writing about code develops both skills simultaneously.

The habit is simple. The results are transformative.

-Leena:)