Most AI-generated games look impressive for about 90 seconds. Then you realize there’s no progression, the difficulty curve is either nonexistent or punishing, and every run feels identical. The problem isn’t the AI — it’s that people skip every step that happens before the first line of code.
If you want to build something actually playable, the workflow matters as much as the model.
Start With a Game Design Doc, Not a Prompt
Professional studios spend months on pre-production: defining the core loop, identifying what makes each run feel different, deciding what the player should feel at minute one versus minute twenty. You don’t need months, but you do need something before you open a chat window.
Write a short design document first. It doesn’t have to be formal. Answer these questions:
- What is the core loop? One sentence. What does the player do repeatedly?
- What creates tension? A timer, a meter, enemies, resource scarcity?
- What creates relief? Power-ups, level-ups, a successful combo?
- How does the player get stronger? Temporary upgrades per run, permanent meta-upgrades, or both?
- What makes run two different from run one? Procedural generation, random events, branching choices?
A concrete example: imagine a café management game where customers arrive in waves, orders pile up on a chaos meter, and you earn XP by filling orders in combos. Temporary upgrades — a faster espresso machine, a shortcut across the counter — reset each run. Permanent upgrades — a larger queue buffer, a loyalty bonus — carry over. That’s a full design doc in five bullet points, and it gives the AI something real to build from.
Use Two Models, Not One
Generation and refinement are different cognitive tasks, and right now different models handle them differently. A capable reasoning model is excellent at spinning up the initial architecture — hundreds of lines of structured code, game state logic, procedural generation, UI scaffolding. But it can be slow and expensive to use for iterative tweaks.
A faster, instruction-following model is better suited for the back-and-forth of tuning: “make the enemy slightly slower,” “fix the camera clipping through walls,” “the shop screen needs a back button.” Paying for heavy-compute generation on every small fix is wasteful and slow.
Practically:
- Write your full design doc into one detailed prompt and let the big model run. Expect it to take a while — that’s normal.
- Test what it produces. Write down every bug and missing feature.
- Switch to a faster model for iteration passes. Feed it your notes and let it work through the list.
Generate a State Document Between Phases
Here’s a step most people skip that saves enormous headaches: before you switch models, tools, or even sessions, ask the AI to write a markdown summary of exactly what it built.
Ask for:
- The full game loop as it currently exists
- Every system implemented (enemies, events, upgrades, UI)
- Known bugs and unfinished features
- File structure overview
This document becomes your handoff. When you start the next session — whether it’s a new model, a new day, or a collaborator — you paste that markdown in and the AI understands the current state instantly. Without it, you waste the first chunk of every session re-explaining what already exists, and the model makes guesses that break things you already fixed.
Balance Is the Hard Part AI Can’t Solve Alone
This is where the “AI is going to replace game developers” take falls apart. Generating a game skeleton takes hours. Balancing it takes weeks, and it requires human judgment at every turn.
Consider a simple chaos meter that fills as things go wrong. If it fills too fast, players feel helpless. Too slow, and there’s no tension. Getting that rate right depends on how fast the player can respond, how many events fire simultaneously, and what the upgrade curve looks like — all of which interact with each other in ways that are genuinely hard to specify in a prompt.
You can ask the AI to adjust values and it will. But you have to play the game, feel what’s wrong, and give specific feedback. “The difficulty spikes too early” is a start. “In the first two minutes, three chaos events fire before the player has unlocked any speed upgrades, making it feel unwinnable” is what the AI can actually act on.
Plan to spend at least as much time testing and giving feedback as you spend prompting.
What Actually Breaks Vibe-Coded Games
Based on the common failure modes, here’s what to watch for during testing:
- Controls that feel inverted or unresponsive — test movement first, before anything else
- Enemies or NPCs with no exit condition — characters that never leave, never get slower, or clip through geometry
- Events with no visual feedback — a tornado that barely affects gameplay, a power-up with no animation
- Meta-progression screens with no navigation — players get stuck on shop or upgrade screens
- Levels that look identical — procedural generation that only changes colors, not layout or feel
Build a test checklist and run through it after every major generation pass. The AI won’t always catch these on its own.
The Honest Takeaway
Vibe coding can get a surprisingly solid game prototype built in a day. The architecture, the boilerplate, the basic systems — AI handles all of that faster than any solo developer could. What it doesn’t replace is the judgment required to make the thing fun: the feel of movement, the satisfaction of a well-timed power-up, the exact right amount of chaos before relief.
That’s not a knock on AI tooling. It’s just an accurate description of where the hard work actually lives in game development — and it was never in the code.