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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →If an AI-generated level feels wrong, don’t regenerate the whole thing by default. First identify the specific failure—such as a disconnected route, a layout that clashes with the game, or an obstacle sequence that feels too demanding—then make a focused edit and test it again. A level can be completable without feeling like it belongs in the game, and automated checks cannot establish whether players find its challenge fair or enjoyable.
Start by naming what feels wrong
Translate “the layout feels wrong” or “the difficulty is off” into something you can inspect. A vague judgment is hard to act on; an observable symptom points toward a useful check or edit.
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- For layout: Are important areas connected? Does the route support the intended objective? Does the arrangement resemble the structures players expect in this game?
- For difficulty: Which demand is causing the problem—the route, obstacles, timing, or a combination? Is a particular section too demanding, or is the level missing a meaningful challenge?
These questions help isolate the problem, but there are no universal thresholds for a good level. What counts as a suitable route or challenge depends on the game and its intended players.
Check validity separately from design fit
First establish whether the level is completable and structurally coherent. Then judge whether its layout and play fit the game’s design language. Those are separate checks: a random tile arrangement may be solvable and still look or feel unlike a level that belongs in the game.
#1 Best Overall
Colan F. Biemer made 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.” [c001]
Make a targeted edit, then run the checks again
Treat generation as an iterative process: inspect the current level, gather structured feedback, plan a constrained edit, and evaluate the result. If the failure is localized, changing a route, room, obstacle placement, or relevant generation parameter is more informative than replacing the entire level. A focused change also makes it easier to tell whether the adjustment helped.
Rank #2
The Agentic PCG project describes an interactive loop in which an agent inspects a game state, plans and edits, then evaluates the result using feedback from the environment. Its examples include structural checks such as tile counts, connectivity, and solvability, as well as behavior-based feedback from a simulated agent. A designer can apply the same basic loop manually: make one purposeful change, run the relevant checks, and inspect what changed. [c002]
Adjust difficulty against a target, not just a score
Difficulty can be treated as a generation target informed by player skill. Biemer describes using a Markov decision process as a director to assemble levels tailored to skill, but the approach was demonstrated with surrogate agents; player studies were planned. That is not evidence that the method improves human players’ experience. [c001]
A separate study of difficulty-adjusted levels in Spelunky used direct and indirect player feedback. Its record reports that most users appreciated online adaptation but were especially critical of making the game easier at any time. The practical lesson is to avoid assuming that reducing difficulty is always welcome: preserve the game’s intended challenge and check adjustments with players. The finding is specific to that game and dates to 2015. [c005]
Simulated play and structural measures can help identify potential problems, but they are diagnostic proxies—not substitutes for human feedback about perceived challenge, fairness, or enjoyment.
Expose controls that correspond to meaningful choices
If the generator offers controls, prefer parameters that let a designer adjust a recognizable feature over an opaque “regenerate” action. In a preliminary dungeon-crawler study, players tried three levels of influence over 22 level-generation parameters. The high-control condition elicited significantly higher reported autonomy. The authors called for further work to separate the effects of agency and challenge, so the result does not establish that more control makes levels better in every game. [c004]
Compare revisions on consistent axes
When choosing between a current level and a revised candidate, use the same game, checks, and feedback method for both. Consider each of these dimensions separately:
Best Value
- Validity: Can the level be completed?
- Structure: Are key areas connected in a way that supports the objective?
- Game fit: Does the arrangement look and play like it belongs in this game?
- Intended challenge: Does the difficulty suit the target player and design goal?
- Player experience: What do people report about the challenge and their control over it?
The cited work does not set shared numeric thresholds for these dimensions. Choose criteria that make sense for your game, and use human play feedback for judgments about how the level feels.
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