Seven months ago, I was a computer science student whose programming education consisted mostly of notes and theory. Today, I've deployed 15 production-grade systems across healthcare, fintech, civic tech, and education—projects that have processed over 80 million AI tokens and would have taken years using traditional development approaches.
This isn't a story about AI replacing developers. It's about understanding how to architect a development workflow where AI amplifies your decision-making rather than substitutes for it. The difference matters.
This isn't "vibe coding" where you throw prompts at ChatGPT and hope something works. Most criticism of AI-assisted development comes from people who don't understand how to properly analyze problems and formulate questions.
The Reality Check
Let me be clear about what this approach isn't: it's not "vibe coding" where you throw prompts at ChatGPT and hope something works. Most criticism of AI-assisted development comes from people who don't understand how to properly analyze problems and formulate questions. The AI isn't magic—it's a tool that requires significant technical understanding to use effectively.
When I built Ereuna, an AI-powered research report generator that creates 200+ page documents with hierarchical organization and intelligent citation management, it took two weeks for the initial build. Without AI assistance, I estimate it would have taken months, and that's assuming I could have even architected such a complex system as a relative beginner.
Ereuna
AI-powered research report generator creating 200+ page documents with hierarchical organization and intelligent citation management
View Project →The difference wasn't that AI wrote all the code—it's that AI helped me make better architectural decisions faster, caught edge cases I wouldn't have considered, and accelerated the iteration cycle dramatically.
My Actual Workflow
Before Writing Any Code
Every project starts with a well-drafted plan that I create independently. This isn't optional—it's the foundation that makes AI assistance effective. For each project, I draft a markdown document containing:
- Feature specifications: What exactly needs to be built, with clear acceptance criteria
- Tech stack decisions: Which frameworks, databases, and tools make sense for this specific use case
- Trade-off analysis: What I'm optimizing for (speed vs. scalability, simplicity vs. flexibility)
- Architecture considerations: How components will interact, data flow patterns, security requirements
I create this plan myself because AI can't make these strategic decisions without understanding your constraints, timeline, and stakeholder requirements. This is where your engineering judgment matters most.
The Development Cycle
Once the plan exists, I use AI at every stage—but differently depending on what I'm doing:
Development Cycle Stages
- Starting from scratch: I work through the plan with AI, making design and critical decisions collaboratively. The AI acts as a knowledgeable peer reviewer.
- Adding features: I describe the feature in the context of the existing codebase, and AI helps maintain architectural consistency.
- Debugging: AI identifies issues in minutes instead of hours. The key is providing enough context about what you've already tried.
- Refactoring: AI helps identify code smells and implement cleaner patterns without breaking existing functionality.
- Learning: When I encounter unfamiliar concepts, I use AI to get up to speed quickly with explanations tailored to my specific use case.
Tool Strategy
I work with a diverse toolkit: Warp, Kilo Code, Cline, VSCode, Microsoft Edge, Terminal, and I rotate between Grok, Claude, and Gemini depending on the task.
I use one tool at a time, but having options means I'm never stuck. If Claude gets into a loop trying to fix a bug, I'll start a fresh conversation with Gemini. Different models have different strengths, and part of becoming proficient with AI is learning when to switch contexts.
This brings up a critical skill that many developers miss: knowing when to start a new chat. If a model seems stuck—repeatedly suggesting variations of the same failed solution—continuing that conversation is counterproductive. Start fresh, reframe the problem differently, or try a different model entirely.
The Horror Stories (And What They Taught Me)
Not everything goes smoothly. I've had AI-generated test code destroy database integrity during development. Fortunately, this happened in the dev environment and I had backups, but it was a stark reminder:
Never trust generated code that touches persistent data without thorough review. The same mistakes could happen with human developers who aren't paying attention. The difference is that AI can generate dangerous code much faster, so your review process needs to be equally fast and thorough.
Another time, while debugging an error, the AI's suggested fix ended up deleting the entire contents of a critical file. Total loss. I only recovered it because VSCode's timeline feature had tracked the changes. Since then, I commit far more frequently and always review diffs before accepting large changes.
The Technical Knowledge Requirement
Here's the uncomfortable truth that the "democratization of coding" crowd doesn't want to hear: AI-augmented development is most powerful when you already understand technical fundamentals.
Project Andrew
Civic tech platform with AI-powered duplicate detection, semantic search, and real-time notifications
View Project →When Project Andrew ran into performance issues with embeddings calculations, I could identify the bottleneck because I understood how vector similarity search works. AI helped me optimize the implementation, but I had to know what to optimize for.
The developers who get the most value from AI are the ones who can recognize when generated code violates best practices, spot security vulnerabilities before they ship, understand performance implications, and know which parts need careful human attention.
The Questions That Matter
The difference between effective and ineffective AI-assisted development often comes down to how you formulate questions.
| Question Type | Example |
|---|---|
| Bad | "My app isn't working, fix it" |
| Good | "I'm getting a 500 error when users submit the contact form. The error log shows 'NoneType object has no attribute split' on line 47 of views.py. Here's the relevant code and the form data that triggers it. What's happening?" |
| Bad | "Make this code better" |
| Good | "This function handles user authentication but it's making three database calls per request. How can I optimize this to reduce database load while maintaining security?" |
The good questions provide context, specify constraints, and demonstrate that you've done some analysis yourself. They treat AI as a collaborator who needs information, not a magic oracle who reads your mind.
Maintaining Code Quality at Speed
My approach to code quality isn't about writing perfect code the first time—it's about having systems that catch problems before they matter.
Quality Assurance Practices
- Automated tests for critical paths: Not everything needs 100% coverage, but authentication, data integrity, and business logic do
- Code review checklists: Even when working solo, I review AI-generated code against a checklist of common issues
- Incremental commits: Frequent, small commits make it easier to identify when and where problems were introduced
- Staging environments: Never test database migrations or destructive operations in production
The Perception Problem
I mentioned that I've been perceived as a senior developer despite having limited coding background seven months ago. This has more to do with the quality of systems I ship than the code itself.
Senior developers are distinguished by:
- Making sound architectural decisions
- Understanding trade-offs and choosing pragmatically
- Shipping reliable systems on schedule
- Communicating effectively with stakeholders
- Learning from mistakes and improving processes
AI can't do these things for you, but it can accelerate every part of the process. I can explore more architectural options in a day than I could have in a week manually. I can implement features faster, which means I get user feedback sooner.
The Numbers
Let me quantify what this workflow enables:
| Metric | With AI Augmentation | Traditional Approach |
|---|---|---|
| Ereuna Timeline | 2 weeks | 3-4 months |
| Projects in 7 Months | 15 | 2-3 |
| Project Complexity | High (production-grade) | Lower (simpler scope) |
Without AI augmentation, I estimate achieving maybe 2-3 of these projects in the same timeframe, and they would have been significantly simpler in scope.
Is This Sustainable?
The question I get most often: "Isn't this just technical debt waiting to explode?"
My answer: It depends entirely on how you work.
If you're blindly accepting AI-generated code without understanding it, then yes, you're accumulating invisible debt. But if you're using AI to accelerate implementation of well-thought-out designs, reviewing everything carefully, and maintaining good testing practices, you're actually in better shape than traditional development.
I've gone back to refactor early projects as I've learned better patterns, and the code quality has been surprisingly good. Not perfect—there are definitely things I'd do differently now—but solid enough that refactoring is straightforward rather than a nightmare.
For Developers Considering This Approach
If you're thinking about integrating AI more deeply into your workflow:
- Start with planning: No amount of AI assistance will save a poorly conceived project. Think through architecture first.
- Build foundational knowledge: Learn enough about your stack to recognize good vs. bad solutions. You can't review what you don't understand.
- Treat AI as a junior developer: Would you merge a junior dev's PR without review? No. Apply the same standard to AI-generated code.
- Experiment with tools: Different models have different strengths. Find what works for your thinking style and problem domains.
- Learn when to start fresh: If you're stuck in a loop, reset the conversation or try a different model.
- Commit frequently: Your safety net is version control. Use it liberally.
- Test critical paths: Not everything needs tests, but authentication, payments, and data integrity do.
- Stay humble: You'll make mistakes. Learn from them, improve your process, and keep shipping.
The Real Value Proposition
This isn't about replacing developers or making coding accessible to everyone. It's about enabling developers who understand their craft to operate at a different scale of productivity.
I can take on projects that would have required a team. I can iterate faster, learn continuously, and deliver value that would have taken much longer through traditional development. But this only works because I've invested in understanding software architecture, design patterns, security considerations, and system trade-offs.
AI augmentation is a multiplier on your existing skills, not a replacement for them. Used strategically, it's transformative. Used carelessly, it's just faster technical debt.
Seven months in, I'm still learning better patterns, discovering new ways to leverage AI effectively, and refining my workflow. But the core principle remains: AI is most powerful when wielded by developers who know what they're building and why.