Using AI Code Assistants to Accelerate Your Development Workflow
Practical approaches to AI in development
There's Always Competition in Development Tools. But Use AI Code Assistants Anyway.
As a developer who's worked with everything from ABAP to Zsh (yes, literally), I've seen countless tools come and go. Today, the tech world is buzzing about AI code assistants, with new options launching almost daily. If you follow ProductHunt, you know exactly what I mean. The competition is crazy right now, but that shouldn't stop you from integrating these tools into your workflow. I was a guest speaker on a Innovator’s Think Tank recently, (you can listen to the recording here) and I discussed the ways to get started on using AI in a Startup. What was so amazing about that experience, was the questions that came up. You should check it out when you get a chance. But here’s some of my thoughts regarding using AI for coding.
Breaking Down the AI Coding Assistant Landscape
When I approach any new technology, I break it down into smaller tasks to understand its full potential. Here's how I view AI code assistants:
Code Completion: They can suggest the next line of code based on context
Code Generation: They can write entire functions or classes based on descriptions
Debugging: They can help identify and fix bugs in existing code
Explanation: They can explain complex code in simple terms
Refactoring: They can suggest improvements to existing code
The Real Value Proposition
Just like I discussed in my previous writings about unicorns vs horses in the startup world, you don't need an AI tool that's perfect at everything. You need one that solves a real problem for you.
For me, working primarily with Python and JavaScript and using GCP as my cloud provider, I've found AI assistants particularly valuable for:
Generating boilerplate code for Flask applications
Creating complex GCP Cloud Functions quickly
Debugging tricky React components
It's Not About Replacing Your Skills, It's About Amplifying Them
Remember when I talked about my journey in public speaking? I mentioned that no one can tell a story exactly as I can. The same applies to coding. AI assistants aren't replacing you—they're joining you on your development journey.
These tools aren't competition; they're inspiration. They help me solve problems I've wanted to solve but wasn't sure how to approach. They suggest things that weren't on my radar. They're mentoring me while I'm mentoring them through my inputs and feedback.
Practical Integration Strategies
Here's how I've integrated AI assistants into my development workflow:
Start with small, specific tasks: Don't ask for an entire application. Start with "Write a function that validates email addresses in Python" or "Help me optimize this GCP Cloud Function for better performance."
Use it for the boring stuff: Let AI handle repetitive tasks like writing CRUD operations, basic validation, or test cases.
Refine through iteration: Provide feedback on what the AI generates, and keep refining until you get what you need.
Learn from its suggestions: When an AI suggests a pattern or approach you hadn't considered, take the time to understand why it made that choice.
The Future is Collaborative
As the CTO of a tech company and someone who's built numerous teams, I've seen how tools evolve. The most successful ones don't replace humans; they augment them. That's what's happening with AI code assistants.
Just like with any team member, the key is understanding their strengths and weaknesses. AI code assistants are excellent at pattern recognition, remembering syntax, and suggesting approaches based on vast amounts of code they've been trained on. But they lack the contextual understanding of your specific business problems, the creative vision to design truly innovative solutions, and the judgment to make those critical architectural decisions.
Start Small, Scale Quickly
My approach to any problem has always been to break it down into smaller tasks and solve them one by one. The same applies here:
Identify one aspect of your workflow where you're spending too much time on repetitive tasks
Experiment with an AI assistant for just that task
Measure the impact on your productivity
If successful, expand to another aspect of your workflow
Tools I use
Here are some of the tools that I have used and played with, in no particular order:
Claude.ai - My go-to prompt writer
ChatGPT - The OG for general prompting and now does images really well
Leonardo.ai - My favorite image generation tool
Replit.com - My favorite full-stack code generator
Softgen.ai - A new one on the scene for web app development
Those are a few that I have used and played with. I’ve pushed Claude pretty far in what it can do and it keeps impressing me.
In Conclusion
There will always be competition in the AI code assistant space, just like there was competition when Google first started competing with AltaVista and Yahoo. But that doesn't matter. What matters is whether these tools can help you build better software faster.
Your coding style, your approach to problems, and your experience make your development workflow uniquely yours. AI code assistants aren't replacing that—they're enhancing it.
As I've always believed, it's not about finding the next BIG thing; it's about finding the next USEFUL thing. For many developers, including myself, AI code assistants have proven to be extremely useful.
What aspect of your development workflow could use some AI assistance? Let me know in the comments!