Definition and scope
Readers can report a factual, definitional, metadata, accessibility, or link error through the contact route. Confirmed material errors are corrected in the page, the updated date is changed, and a concise note is added when the correction alters the meaning of a claim or definition.
A category resource becomes more useful when errors and revisions are treated as part of the publication record rather than hidden maintenance.
Why the distinction matters
The policy is designed to make ownership, claims, sources, and revisions inspectable.
For explain corrections process, the decisive question is what information the system can use, what role it performs, how long the context remains relevant, and what the player can inspect or control.
How to apply the idea
Reports should include the URL, disputed text, reason, and a primary source where relevant; the operator reviews conflicts of interest.
Start with the smallest useful context. Name the relevant game, run, character, or interaction horizon; distinguish verified facts from player statements and model inference; then link the result to a visible source or restore point when it must remain durable.
- Identify the primary player need.
- Choose the companion type or comparison level.
- Define inputs, authority, retention, and deletion.
- Test the likely failure mode, not only the ideal response.
Boundaries and caveats
A disagreement with editorial framing is considered, but it is not automatically a factual correction.
Product labels are not enough evidence. Current features, privacy behavior, platform access, and compatibility should be checked in official documentation. A fluent response can still contain an incorrect fact, stale state, or a plausible merge of two different runs.
A practical evaluation model
Evaluate a companion across five dimensions: context input, system role, action authority, time horizon, and player control. Add source quality and privacy when the system uses external knowledge or stores durable state.
This model keeps interface features in perspective. Voice, screen capture, an avatar, and a dedicated app can improve a particular implementation, but none of them defines the entire AI gaming companion category.
Frequently asked questions
What is the shortest explanation of “Corrections Policy”?
Readers can report a factual, definitional, metadata, accessibility, or link error through the contact route. Confirmed material errors are corrected in the page, the updated date is changed, and a concise note is added when the correction alters the meaning of a claim or definition.
Does this require real-time screen access?
Not necessarily. Screen access is an input used by some coaching systems, not a requirement for the wider category.
What should a player or product team verify?
Verify the system’s actual inputs, action authority, source quality, privacy controls, retention, correction path, deletion behavior, and whether its visible product claims match its implementation.
Further reading
Contact AI Gaming Companion
Use the published contact channel for corrections, source updates, accessibility problems, taxonomy feedback, and editorial enquiries. Include the affected URL and enough context to review the request. Do not send passwords, private game account data, unpublished personal information, or sensitive save-state content.
Editorial Policy
The editorial policy requires clear definitions, observable distinctions, primary sources for current product claims, dated updates, visible disclosures, and corrections when material errors are found. It prohibits fabricated citations, fake reviews, unsupported leadership claims, copied competitor language, and paid conclusions presented as independent analysis.
Methodology for the AI Gaming Companion Taxonomy
Definitions are based on observable product architectures and conceptual distinctions: what context a system receives, what role it performs, whether it can act, where it exists, how long context remains useful, and what control the player retains. Screen coaching is treated as one subtype, not the category boundary.
About AI Gaming Companion
AI Gaming Companion is a category publication about the systems that guide, interact with, act for, socialize with, or preserve continuity for players. It is operated by Raynor Eissens, creator of Companion Play, and separates broader category analysis from discussion of that implementation.
Raynor Eissens — Author and Project Architect
Raynor Eissens is the creator of Companion Play, author and project architect of AI Gaming Companion, and the publication operator. His work here focuses on gaming continuity, LLM-native interfaces, conversational save state, and a broader category model for AI gaming companions.