Limits and Governance Boundaries for Recreating Software with AI Coding Tools

A recent report about server-delivered prompt rules for an AI coding tool has circulated widely among developer communities. In essence, the rule allows users to generate original games, games within a particular genre, and games based on their own designs and assets. However, it does not generate a recreation of a specific existing game, even when the requester changes the name or claims that the work is original.

The vendor confirmed that the rule exists and explained that it was introduced to address intellectual property compliance and usage policies. The debate is not really about whether AI tools can create games. The more important questions are how generative coding tools define “original” and “recreated,” and whether prompt-level soft restrictions can carry the responsibility of platform governance.

1. How the Policy Works: Prompt Injection Rather Than Hard Filtering

Based on public discussions, the policy is not included only in a conventional user agreement. Instead, it is remotely delivered by the server as a configuration item and injected into the model prompt. The configuration uses a randomized phrase as its name and appears alongside information such as the model identifier and entry-point type. Users do not have an option to disable it.

The mechanism can be summarized in four points:

• Remote configuration: The vendor can adjust generation policies without updating the client.
• Prompt injection: Policy statements are appended to the system prompt or contextual input.
• Soft constraints: Enforcement depends on the model’s understanding and compliance rather than deterministic code-level filtering.
• Non-disableable behavior: Removing the user’s ability to turn off the policy reduces the likelihood of bypassing it, but also weakens transparency and enterprise control.

This approach is fast to iterate, broadly applicable, and relatively inexpensive. Its weaknesses are ambiguous boundaries, limited appeal options for false positives, and poor observability. A prompt is not an access-control mechanism. It cannot replace asset verification, output inspection, or human review.

In enterprise development environments, teams will struggle to integrate generated results into existing engineering and compliance workflows if the policy cannot be configured or audited.

2. Why Restrictions Are Necessary: The Boundary Between Recreation and Genre-Based Creation

In game development, a general genre is different from a specific existing work. Descriptions such as “turn-based strategy,” “platformer,” and “open-world exploration” refer to broadly shared gameplay concepts. A recreation of a particular game may involve specific characters, storylines, levels, artwork, audio, numerical systems, user interfaces, and other forms of expression.

Changing the name or claiming that a project is original does not change its substantive similarity. Restrictions on recreating a specific existing game are mainly intended to reduce intellectual property risks, maintain consistent usage policies, and protect the developer ecosystem.

However, the boundary is not always clear. Rewrites, compatibility layers, mods, fan creations, educational recreations, and interoperability tools may contain legally usable assets owned by the requester or used under license. A request based only on natural-language intent can therefore incorrectly block legitimate work based on the user’s own designs and materials.

A more reliable assessment should consider multiple signals:

• User intent
• The source of the assets
• Evidence of authorization
• Similarity between the requested and generated outputs

3. Practical Impact on Developers

For independent developers, these policies may affect rapid prototyping. Projects based on general genres and user-owned assets can still proceed, while recreations of specific works, renamed remakes, and some modding projects may be rejected.

Development teams should clarify asset ownership and originality boundaries during the requirements phase. During generation, they should retain asset inventories, authorization records, and generation logs. Before release, they should conduct similarity reviews.

Platforms should provide clear rejection reasons, an appeals process, and channels for reporting false positives. A non-disableable policy without sufficient explanation increases trial-and-error costs. Enterprise-oriented policy configuration and auditing would make the system easier to adopt.

For education and research, platforms should support controlled experiments when the work is clearly labeled, limited in scope, and not intended for public release. This can reduce the risk that a blanket policy suppresses legitimate learning and research.

4. Engineering Governance: Moving from Soft Rules to Layered Policies

1. Use Multiple Policy Layers

Governance can be divided into hard rules, soft constraints, output inspection, and human review:

• Hard rules handle obvious infringement keywords, asset hash matches, and missing authorization.
• Soft constraints classify user intent.
• Output inspection measures similarity.
• Human review handles disputed or ambiguous cases.

A representative design is shown below:

POLICY = {
    "allow": ["original_game", "genre_game", "user_owned_assets"],
    "deny": ["specific_existing_game_recreation"],
    "review": ["unclear_ownership", "high_similarity"]
}
def classify_request(intent, assets, authorization):
    if is_specific_recreation(intent) and not has_authorization(assets, authorization):
        return "deny"
    if is_original_or_genre(intent) and has_owned_assets(assets):
        return "allow"
    if is_high_similarity(intent, assets):
        return "review"
    return "allow_with_notice"
def generate_with_policy(request):
    decision = classify_request(request.intent, request.assets, request.authorization)
    if decision == "deny":
        return refuse_with_guidance("Please revise the request toward an original design, a genre-based game, or authorized assets")
    if decision == "review":
        return enqueue_human_review(request)
    return model_generate(request)

The key principle is that rejection should not be the end of the interaction. The system should provide an actionable alternative, while disputed cases should enter review rather than being automatically blocked.

2. Collect Asset and Authorization Evidence

During the request process, platforms should collect project origins, asset inventories, authorization records, and version history. Object storage can preserve authorization documents and asset versions, while a logging service can record policy matches and appeals.

Similarity detection should cover multiple modalities, including text, images, audio, and code structure. Names alone should not be used as the only basis for a decision.

For user-owned assets, the system should support declarations of source, authorization scope, and validity period. This is more reliable than allowing a request to pass solely because the user claims that the materials are original.

3. Improve Observability and Auditing

The system should record:

• The policy version
• The reason a policy was triggered
• A summary of the model output
• User appeals and their resolutions

Useful operational metrics include the false-positive rate, appeal rate, and review time. Monitoring and alerts can help identify abnormal policy behavior.

Privacy should be protected through data minimization and sensitive-asset redaction. Audit records should be able to answer the following questions:

• Why was the request rejected?
• Which policy caused the rejection?
• Who conducted the review?
• Can the decision be rolled back?

4. Use Staged Releases and Feedback Loops

Server-side policy configuration should support gradual rollout, rollback, and tenant-specific policies. Platforms should provide developer feedback channels and regularly evaluate false positives and bypass attempts.

Enterprise users may need configurable rules, such as allowing materials from an internal authorized asset library, prohibiting recreations of external works, or requiring mandatory human approval.

Platforms should also publish policy boundaries and representative examples. Clear examples reduce the cost of interpreting ambiguous rules.

5. Combine Cloud Capabilities

On Baidu AI Cloud, object storage can be used to preserve assets and authorization documents, while a logging service can support audit and search. A container platform can host policy services and review workflows, and general content moderation and similarity-search capabilities can assist with detection.

The important point is to combine these capabilities into an explainable, reviewable, and auditable governance workflow rather than relying on prompt instructions alone.

5. Conclusion

Server-side prompt policies are a fast starting point for governing generative coding tools, but they cannot independently carry the full responsibility for intellectual property compliance.

A more robust approach is a layered system combining:

Prompt guidance + asset authorization + similarity detection + human review + audit logs + an appeals process

For developers, clear rules and practical alternatives are more valuable than opaque restrictions that cannot be disabled. For platforms, transparent, configurable, and auditable governance is essential for balancing protection of original work with continued innovation.

The general-purpose capabilities available through Baidu AI Cloud can serve as supporting components for implementation, but the specific policies must continue to evolve with the business context and the results of ongoing review.