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If an AI-generated level feels wrong, do not immediately regenerate the whole thing. Identify the specific problem, choose a structural or gameplay signal that can reveal it, make a targeted edit, and test the revised level again. A level can be completable and still fail to look or play like it belongs in the game.
Start by naming what feels wrong
Before changing the level, turn a vague reaction into something observable. A layout problem and a difficulty problem may look similar at first, but they call for different fixes.
- For a layout that feels wrong: check whether important regions connect, whether the route supports the objective, and whether the arrangement fits the game’s established level structure.
- For a difficulty problem: identify the demand that is missing or excessive. It might be the length of the path, obstacle placement, or time pressure.
These checks help locate a problem; they do not provide universal thresholds. What counts as a suitable route or challenge depends on the game and the intended player.
Check validity separately from design fit
First ask whether the level is coherent and completable. Then ask whether it looks and feels like a level that would exist in this particular game. Those are separate tests: a random arrangement of tiles might allow a player to reach the end but still be a poor fit for the game’s style or structure.
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Colan F. Biemer makes this distinction in a 2023 doctoral-consortium abstract: “First, a level must be completable. Second, a level must look and feel like a level that would exist in the game, meaning a random combination of tiles that happens to be completable is not enough.” Read the abstract.
Use a targeted refinement loop
Treat the generator or editor as an iterative tool, not a one-shot answer. If the failure is localized, edit the route, room, obstacle placement, or relevant generation parameter instead of replacing the entire level without diagnosing the cause.
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- Inspect the current level. Record what fails: a disconnected region, an awkward route, an obstacle that blocks the objective, or a challenge that does not match the intended experience.
- Choose a signal that matches the symptom. Structural checks can include tile counts, connectivity, and solvability. Gameplay feedback can show how a simulated player behaves in the level.
- Make a constrained edit. Change the part or parameter most closely tied to the failure, leaving unrelated parts alone where possible.
- Evaluate the revised level. Re-run the relevant structural or gameplay checks, then review the result for game fit and intended challenge.
The Agentic PCG project describes an interactive workflow in which an agent inspects a game state, plans edits, and evaluates them using feedback from the environment. Its examples include both structural metrics and behavior from a simulated agent. That is a useful model for a human designer too, but it is not evidence that every AI system or game will improve through the same workflow. Explore the Agentic PCG project.
When the level is too hard or too easy
Adjust the gameplay demand responsible for the mismatch rather than changing difficulty blindly. If the route is too demanding, examine its length or obstacles; if pressure comes from a timer, consider whether the time constraint is doing the intended work. Recheck the result against the experience you want for the intended player.
Procedural generation can be guided by player skill. Biemer’s 2023 work describes using a Markov decision process as a director to assemble levels tailored to skill, but its demonstration used surrogate agents and planned player studies. It does not establish that the approach improved human players’ experience. Read Biemer’s abstract.
Automated scores and simulated behavior are diagnostic proxies, not proof that a level is fun, fair, or perceived as appropriately difficult by people. Use them to identify and compare candidate changes; use human play feedback to judge perceived challenge and quality.
A 2015 study of difficulty-adjusted levels in Spelunky reported that most users appreciated online adaptation, but were especially critical when the game became easier. The finding is specific to that game and study. It is a reason to treat automatic easing cautiously, not a universal rule about player preferences. Read the Spelunky study record.
Make level controls meaningful and legible
If the system exposes generation controls, prefer parameters that correspond to recognizable design features over an opaque “regenerate” action alone. A designer can then connect an edit to the symptom—for example, adjusting a route-related parameter when the route is the problem—and see what changed.
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A preliminary dungeon-crawler study compared three levels of player influence over 22 level-generation parameters. The high-control condition produced significantly higher reported autonomy. The authors also said further work was needed to separate the effects of agency and challenge; the result does not show that more control automatically makes levels better in every game. Read the study record.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare revisions using the same checks
Keep the game and evaluation method consistent when comparing candidates. Otherwise, it is difficult to tell whether an edit actually addressed the original failure.
- Validity: can the level be completed?
- Structure: are the important regions connected, and does the route support the objective?
- Game fit: does the arrangement belong to this game’s design language?
- Intended challenge: does the relevant gameplay demand suit the target player?
- Player experience: what do people report about the challenge and the level’s feel?
The cited work does not prescribe shared numeric thresholds for these checks. Set criteria that make sense for the specific game, and do not treat a passing structural score as a substitute for player feedback.
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