What Data for This Category Looks Like
This category’s data primarily comes from game text, story scripts, and art assets created during game development and launch, alongside publicly released regulatory compliance requirements. Daily associated business documents include content self-inspection forms, sensitive content modification records, user agreements, and compliance review minutes. Data updates are adjusted based on game version iterations or event launch timelines. A single update covers all compliance-related materials for one version or one event. Data fields include items to be inspected, compliance status, modification records, review comments, and other relevant details. No fixed unified units exist. The length and number of items per material vary based on content scale.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
Since this category’s data spans multiple material types including game text, story scripts, and art assets, multi-turn dialogue must support context association across material types. Prompt engineering must clearly delineate compliance inspection rules for different materials. Since data updates align with game version or event schedules, the dialogue flow must adapt to full inspections within a single cycle, and must not retain cross-version historical context. Since daily business documents include content self-inspection forms and sensitive content modification records, prompt engineering must embed field verification logic, such as matching past modification tracks for sensitive content. Since a disputed content modification and review closure workflow exists, multi-turn dialogue must support cyclic verification, associate past review comments, and avoid repeated processing of the same compliance issues. Unlike one-time processes such as game edition number application and publication declaration, compliance self-inspection for this category runs through the entire version iteration cycle. As such, multi-turn dialogue must support staged context retention and updates.
How to Configure Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind to the unique identifier of a single version or single event | Ensure dialogue context within the same cycle does not interfere across versions or events |
messages | Arrange material entries in the order of compliance inspection execution | Ensure multi-turn dialogue verifies content sequentially according to the self-inspection, review, and modification workflows |
variables | Embed field variables from content self-inspection forms and sensitive content modification records | Quickly call preset compliance rule parameters from business documents |
| Maximum number of context entries | First 15–25 entries | Adapt to the context scale of single-version compliance inspections, avoid exceeding processing limits |
| Context retention duration | 7–10 days | Cover the full cycle from self-inspection to launch for a single version |
| Cyclic trigger condition | Trigger when verification fails twice consecutively | Match the multi-round modification and review closure workflow for disputed content |
Three Common Misconfigurations
- Phenomenon: After setting 30 context entries, only 2 context entries appear in the dialogue details, and replies cannot link to historical inspection content. Cause: The
messagesparameter is not bound to the current version’s material list in compliance inspection order, so the system only loads a small number of temporarily cached entries. - Phenomenon: The completion reason field for compliance checks is empty, and subsequent workflow nodes cannot read the field value. Cause: The
variablesparameter is not configured in the AI dialogue node to bind the completion reason output field, so the field is not correctly written to the context. - Phenomenon: The prompt cannot recognize spaces in compliance text, leading to missed sensitive content inspections. Cause: The prompt does not explicitly require retaining space formats in text, so the model misidentifies content with spaces.
How to Confirm Proper Configuration
- In FastGPT v4.17.0, run a compliance inspection for a single piece of game text, and confirm that the number of context entries displayed in the dialogue details matches the configured maximum number of context entries.
- Trigger the cyclic verification workflow for disputed content, confirm that a compliance check restarts when verification fails twice consecutively, and that past review comments are associated.
- Call the business document field bound to the preset
variablesparameter, confirm that the prompt can correctly call compliance rule content. - Check whether the completion reason field of the compliance check is correctly written to the context, confirm that subsequent workflow nodes can read the field value.
All provided configuration values are common starting points. Adjustments should be made based on individual specific samples and use cases.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.