Review AI-generated code changes in a game project
Use a practical review checklist for scope, diffs, permissions, engine validation, and evidence before accepting AI-assisted game code.
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AI-generated code is a proposal, not proof that a game change is safe or correct. A useful review checks what was requested, what changed, whether the change runs in the target engine, and whether the result meets the original acceptance criteria.
1. Describe a small, observable result
Give the task a concrete outcome and a short list of files or systems it may affect. For example: “Add a reset key to the Lantern Workshop player. Pressing R should restore the player to the start position. Do not change input mappings, scene layout, or collectible behavior.” This gives the reviewer something specific to compare with the diff and the running game.
Keep feature design, code changes, and asset generation as separate requests when they need different tools or acceptance checks. If a task discovers a missing requirement, answer the question and update the request before expanding its scope.
2. Check the workspace and permissions
Confirm the selected project, branch or worktree, and starting state before the task begins. A clean, disposable workspace makes it easier to identify which files changed and to recover from an unwanted result. Read any impact preview, file scope, role, and tool-approval prompt before granting access.
Provider choice matters too. A local model and a cloud model do not have the same data path, and model capability does not establish code quality. Keep credentials out of project notes and screenshots. See AI model setup and routing for provider and routing checks.
3. Read the complete diff
Review every changed file, not only the agent's summary. Check that the change matches the request, uses the project's existing patterns, and does not alter unrelated configuration, dependencies, generated files, or native game assets. Look for hard-coded paths, hidden network calls, broad permission changes, missing error handling, and behavior that was not requested.
For generated textures, models, or audio, inspect the actual file and its import settings. A completed generation job does not prove that an asset is licensed for your use, imports correctly, or works in the game. PlayWeld's asset review guide separates those checks.
4. Run the checks the game actually needs
Use the engine and checks that match the change. A successful text edit or headless import does not prove that the game launches or plays correctly. Open the project in its target engine, run the relevant scene or test, and verify the behavior described in the acceptance criteria. Check neighboring input, save data, and scene transitions when those could be affected.
If a live editor bridge or build tool is unavailable, record that as an unperformed check. Do not treat a configured engine path, task completion message, or generated screenshot as evidence that an executable game test passed. The engine connections guide describes the separate file, headless, and live-editor layers.
5. Accept only the inspected result
Compare the final result with the request, review test output and artifacts, then integrate the change deliberately. If the change is wrong, keep the diagnostics and discard or repair the isolated work rather than copying unknown files over your working game. After accepting document changes, refresh project knowledge where needed.
PlayWeld records task questions, approvals, tool calls, and integration state so you can inspect parts of the workflow. Those records do not guarantee that code is secure, correct, or fun to play; the engine's own checks and your review remain necessary. Follow the supervised agent workflow for the PlayWeld-specific review path.
Try the workflow
Use a disposable project first. Lantern Workshop is a small Godot learning project with testable movement and reset behavior. Read the getting started guide or choose a PlayWeld setup before trying it. PlayWeld is a free, open-source desktop IDE; its current downloads are testing prereleases, and AI provider usage may have separate costs.