I’m a developer who has been working with AI coding tools for about a year now. My role has shifted from writing code directly to more of a planning and architecture focus, then collaborating with AI agents to implement the solutions.
I started with basic chat interfaces, then moved to tools like Cline for API work. Now I mainly use Claude Code and have found a workflow that works really well for me.
Here’s my typical process when building new features (both backend APIs and React frontend):
I have Claude help me brainstorm and create a detailed implementation plan that even a junior developer could follow.
During planning, I make sure it understands our existing interfaces, API contracts, and database structure.
Once the plan is solid, I ask it to write test cases along with basic function stubs.
Then I have it work through a checklist approach, implementing each piece until all tests pass.
This test-driven approach has been incredibly effective. I can literally tell the AI I’m stepping away and it will work through the entire checklist. I’ve been able to ship fully tested production features in just 2-3 days this way.
I’m curious about what other workflows people have discovered for getting high-quality code output from AI assistants. What approaches have worked well for you?
Your test-driven approach totally matches my experience, but I’ve found pair programming with AI beats full automation every time. I bounce between coding sessions where we work together and review sessions where I step back and check what we built. The game-changer was setting clear handoff points. When I need the AI to tackle something complex solo, I give it super detailed acceptance criteria - not just what to build, but edge cases and performance requirements. Then I always review afterward to catch the logical gaps it missed. One pattern that’s saved me tons of debugging time: I make the AI explain its approach before writing any code. If the explanation doesn’t click for me, the code won’t either. Catches architectural problems before they turn into tech debt. Context management got way easier once I started keeping a project-specific prompt template with our coding standards, common patterns, and known issues. Saves setup time and keeps quality consistent across features.
AI debugging has transformed my workflow more than anything else. Most devs I know struggle when AI-generated code breaks in production, but I’ve figured out a solid approach. When bugs pop up, I don’t just throw error messages at the AI. I walk it through my debugging process step by step - tracing execution paths, checking state changes, finding where assumptions fell apart. This collaborative approach catches root causes that straight code generation completely misses. Here’s another trick: I have the AI write ‘explaining comments’ throughout complex logic, then delete them later. Forces it to explain its reasoning, which exposes bad assumptions early. I’ve caught tons of edge cases this way before customers ever see them. The real game-changer was building debugging into my initial workflow instead of treating it as cleanup. Now when AI implements features, I immediately have it walk through failure scenarios and add proper error handling. Takes a bit longer upfront but saves me hours when things break later.
Nice workflow! I’ve been doing similar stuff but went full automation on the whole pipeline.
Game changer for me was automating all the repetitive AI dev work. No more manually juggling planning, testing, and implementation - I built automation that runs the entire cycle.
Here’s how it works: I kick off a new feature and my system pulls the latest codebase, generates the plan, creates tests, and monitors the AI through each step. It even handles code reviews and deployment prep.
Best part? I start the process and come back to fully implemented, tested features. No babysitting the AI or watching it lose context halfway through.
Cut my dev time from days to hours since automation handles all the coordination. AI focuses on what it’s good at while the workflow keeps everything moving.
You should try building something like this with Latenode. Perfect for orchestrating dev workflows and connecting AI tools.
AI became way more useful once I started treating it like a rubber duck that writes code.
I front-load way more context than most people bother with. Not just tech specs - I explain why we’re building this, what’s bitten us before, and what the business actually wants.
Then I work backwards from UX. Have AI mock up API responses first, build the frontend against those mocks, then implement the backend. Sounds weird but catches integration problems early.
Another time-saver: get AI to write integration tests before unit tests. Unit tests are fine but miss the weird stuff that breaks when systems connect.
I keep a text file of “AI blind spots” per project. It always forgets our auth flow or suggests deprecated libraries. Just paste that into each session and skip repeating the same mistakes.
Biggest lesson: AI’s great at building but awful at knowing when to quit. Define exact scope upfront or it’ll “improve” stuff that already works.
I’ve been trying a different approach - skip full automation and focus on keeping context intact. My main problem was AI forgetting architecture decisions halfway through coding. Now I keep a running “decision log” that goes into every prompt. Keeps the AI on track between sessions and stops architectural drift. Breaking complex features into small atomic pieces works way better than massive checklists too.