I’m curious about how other developers integrate AI language models into their coding routine. I’ve been experimenting with different approaches but want to hear from the community about what works best.
What tools and extensions do you rely on? Do you have a specific method for prompting the AI to get better results? I’m particularly interested in learning about any workflow optimizations you’ve discovered.
For example, do you use the AI for initial code generation and then refine it manually, or do you prefer to write the basic structure first and then ask for improvements? Also wondering about debugging approaches - do you feed error messages directly to the AI or try to solve issues yourself first?
Would love to hear about your setup and any tips that have made your development process more efficient.
I treat AI like a rubber duck that codes back. When I’m stuck, I explain the problem out loud like I’m talking to a coworker - it forces me to think clearly while getting actual suggestions. My sweet spot? Using AI for exploration, not generation. I ask for three different approaches to the same problem, then cherry-pick the best parts. Gives me options I’d never think of and shows me the trade-offs. For bugs, I always ask why an error happens before asking for the fix. Understanding the root cause has made me way better at debugging. I also paste my functions and ask what could break or fail - catches edge cases I miss when I’m too deep in the code.
Been doing this for years and most people overcomplicate it.
I keep it simple - AI handles grunt work, I do the thinking. When building a new feature, I sketch the architecture and core logic first. Then AI writes tests, docs, and boilerplate.
The key is knowing when NOT to use it. Complex business logic? Algorithm optimization? Database design? I handle those. AI gives generic solutions that miss your specific problem’s nuances.
For debugging, I have a rule - can’t spot the issue in 10 minutes? I paste the error and code into AI. But I always validate suggestions before applying them. Too many AI fixes work initially but create subtle bugs later.
Game changer for me was building a personal prompt library. Templates for code reviews, refactoring, test generation - each one refined from past projects.
My setup’s basic - standard extensions plus custom scripts to format code before sending to AI. Nothing fancy, just gets the job done without slowing me down.
I’ve been using AI for coding and the biggest win? Stop doing everything manually and automate the whole thing.
I set up workflows that handle the boring stuff automatically. Push code → AI reviews it → suggests fixes → applies simple ones without me lifting a finger.
Doing it manually sucks. You’re constantly writing prompts, copying code around, juggling different tools. My automation feeds the AI my style guide and project context automatically, so it already knows what I want.
For debugging, my workflows catch errors in real time and run them through AI analysis before I even see them. The AI gets full context - codebase, recent changes, error patterns. Much better than going back and forth manually.
Treat AI like another service in your dev pipeline, not a chatbot. Automate everything and let it work in the background while you handle architecture and business logic.
I use Latenode for this - it connects AI models with dev tools and handles workflow orchestration. Check it out: https://latenode.com
Honestly? I use AI mainly for documentation and breaking down messy legacy code. Too many devs have AI write whole functions right off the bat - that’s backwards. I’ll throw cryptic code at it and ask ‘what’s this actually doing?’ Saves me hours of puzzling it out. Also perfect for quick regex and SQL queries when I’m mentally fried.
After using AI coding assistants for about a year, I’ve found a workflow that clicks for me. I write the function signatures and basic structure first - the AI works way better when it has real context to grab onto. Then I let it handle the implementation details. The biggest lesson? Be super specific upfront about what you need. Don’t just say ‘write a function to sort data.’ Tell it the data type, input size, performance needs, and any edge cases you’re worried about. Saves tons of back-and-forth tweaking. For debugging, I dig into the problem myself first, then bring in the AI as a second set of eyes. I’ll drop in the code chunk, error message, and what I think might be broken. Way better for actually learning than just dumping errors on it right away.