What the data for this category looks like
Core data for level content creation comes from level editors, art resource libraries, player test data, and defect systems. Daily produced documents include level design drafts, resource requirement lists, test feedback sheets, and version submission records. Common fields include level ID, path node coordinates, resource reference paths, test pass rates, and version numbers. Data is updated weekly or once per version iteration. Each document must be associated with multiple types of resources and test feedback to form a complete business chain.
What constraints these characteristics impose on the multi-turn dialogue and prompt engineering link
Data sources are scattered and updates are frequent, so multi-turn dialogue must maintain stable context to associate historical requirements and feedback. The professional nature of document fields requires prompts to explicitly specify business-specific fields, avoiding content that does not comply with game development specifications. Multi-round iterative requirements require dialogue flows to support gradual corrections. Context length must be controlled to avoid exceeding model limits. Errors in level content directly affect launch schedules, so configuration must constrain the accuracy and format compliance of dialogue outputs.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Dialogue context window | 20000–30000 token, calibrated based on actual testing | Adapts to the context length of a single complete level design draft and multiple rounds of test feedback |
| Variable binding | Bind level ID and resource type fields | Ensures dialogue can associate specific business data and avoid generalized outputs |
| Historical message truncation threshold | Retain the first 8–12 core dialogue entries | Balances context completeness and model input costs |
| Prompt template | Explicitly specify "polish and expand based on level design draft format" | Aligns with the structural requirements of business documents and avoids output deviating from the scenario |
| Session ID association | Bind the unique identifier of the current level | Ensures multi-turn dialogue only associates context for the current level and avoids cross-session confusion |
Three Common Misconfigurations
- File uploads in the dialogue window are never parsed, and no errors appear in the backend: Variable binding for file parsing fields is not configured correctly, or the AIPROXYAPIENDPOINT (root address of the AI Proxy service) and AIPROXYAPITOKEN (AI Proxy admin token) environment variables for the App/Admin backend are not configured correctly.
- Current dialogue cannot be stopped: No session context cleanup node is added to the workflow, or the session ID parameter is not passed correctly to trigger context reset.
- Multi-turn dialogue output is not polished and expanded as required: The prompt does not explicitly specify the target document type as a level design draft, causing the model to output content that does not meet business format requirements.
How to Confirm Configurations Are Correct
- Check the environment variable configuration for the App/Admin backend, confirm that AIPROXYAPIENDPOINT (root address of the AI Proxy service) and AIPROXYAPITOKEN (AI Proxy admin token) are both set and non-empty, and comply with the deployment requirements of FastGPT v4.17.0.
- Initiate a test dialogue, input the requirement "supplement path node coordinates for the novice level", and check whether the returned result contains content that conforms to the level design draft format.
- Upload a test resource requirement list, and initiate a multi-turn dialogue to verify whether the context can associate the previously uploaded file content.
- Trigger a session reset operation, confirm that the dialogue context is cleared, and new dialogues will not carry redundant information from previous sessions.
The values provided here are common starting points and should be measured against the reader's own samples.
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.