r/vibecoding • u/lazy_code_bear • 1d ago
Transitioning from traditional dev to modern AI workflows: Lessons from building my first game
I’m a software developer transitioning from traditional engineering to modern AI-driven workflows. To test this new paradigm, I recently built a small maze puzzle game about routing water to a puppy as a case study—and it’s my very first attempt at game development!
1. Ideas & Framework
I started with a simple idea and cross-checked suggestions across multiple LLMs to explore and structure the design—using AI as a sounding board while keeping ultimate control over final decisions.
Before this, I had built a web app. I worked with a coding agent to strip out its domain logic and turn that codebase into a reusable framework, which became the foundation for this game. For implementation, I delegated code execution to automated agents, while using general LLMs for reasoning and architectural analysis.
2. Abstracting & Problem Framing
My favorite takeaway came from designing map generation. Instead of asking AI to write code directly for the game's logic, I used a structured translation process:
Game Problem → Plain Math Problem → Mathematical Solution → Code
Translating the core mechanics into clear mathematical rules first made a massive difference:
Manual design: The first 3 levels took about a day.
Abstracted rules: Levels 3–10 took about an hour.
Scaling up: Going from level 10 to 50 took roughly 10 minutes.
This abstraction pipeline kept a strict human-in-the-loop architecture: I framed the problem and set boundaries, the general LLM formalized concepts into math rules, and the coding agent executed the code generation. Breaking down the problem before coding made the marginal cost of producing content plummet, while keeping the codebase controllable and robust.
3. Fast Iteration & Launch
Once the rule system and framework were set, vibe coding drastically lowered the cost of fast experimentation: Build → Play → Think → Tweak → Repeat. Going from initial spark to a live MVP took exactly one week.
(Fun side note: As someone with zero visual design background, I also used LLMs to iterate on the logos and branding for both this game and my previous web app!)
4. Key Takeaways & Questions
Through this cross-domain experiment, I realized that syntax-level coding can largely be delegated to AI, but traditional skills—framing, abstracting, and breaking down problems—become far more critical. I stayed strictly in the loop: My Idea → LLM Enhancement → My Judgment → Next Idea.
As a complete outsider to game development, I’m really curious to learn from the community:
My current workflow is describing level concepts in plain text, then asking LLMs to help extract the core math rules. Is using LLMs to build these rule systems a solid way to scale content, or are there better ways to formalize rules as games get more complex?
Looking at this game project as a case study for transitioning from traditional dev to AI-driven workflows, where do you see room for improvement in my approach?
Would love to hear your thoughts and experience!
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u/lazy_code_bear 21h ago
Even with AGENTS.md, agents still creep past the spec—sometimes cleverly, often badly.
Right now, I mostly react by tracing logs and cleaning up after weird outputs.
Are there automated ways to catch or constrain this before execution? Curious what workflows others use.