AI generation vs. procedural generation in puzzle design

Two different ways to scale

Procedural generation is mathematical and deterministic. When a game generates a dungeon layout using cellular automata, it's following a strict set of human-authored rules. It scales by running those rules countless times. AI generation, particularly with large language models, operates on statistical probability. It scales by interpolating patterns from vast datasets. For a puzzle game, these two approaches pull in opposite directions.

The problem with statistical puzzles

A good logic puzzle requires absolute certainty. If a player deduces a solution, the system must honor the logic perfectly. Procedural generation can be carefully constrained to guarantee solvable puzzles—like generating a Sudoku board. AI generation, however, is prone to "hallucinating" or bending rules, making it fundamentally unreliable for creating the strict, interconnected logic constraints that define an elegant puzzle. It cannot be trusted to generate the puzzle itself.

Where AI actually belongs

Instead of generating the game's content, AI belongs in the tooling that helps build the game's engine and systems. It accelerates writing the procedural algorithms. We use AI to scaffold the level editor, write testing scripts, and quickly prototype mechanics. Procedural generation builds the levels; AI builds the tools that build the levels.