What This Category’s Data Entails
The daily data for game design and numeric balancing includes four core document types: gameplay design documents, numeric tables, version requirement sheets, and balance test records. Data sources typically include player behavior data, public competitor materials, battle logs, and version feedback. Data updates occur weekly or per version iteration. Numeric tables contain fields with units such as damage coefficients and growth curve parameters. Gameplay design documents include rule descriptions and test scenario entries. Balance test records are categorized by defect type and impact scope.
Constraints Imposed on Workflow Orchestration
The core data for game design and numeric balancing combines structured numeric values and rule text, with a high update frequency. This requires workflows to support dynamic integration of multiple data sources. Fields vary widely across different document types: numeric tables require precise matching of parameter units, and gameplay design documents require splitting rule nodes for processing. Workflows must therefore include differentiated nodes to adapt to different document types. Data errors can lead to gameplay imbalance or version rework, so workflows must add pre-checks and failure rollback links. Workflows must also support rapid iterative adjustments to match weekly or version-level update rhythms.
Configuration Settings
The following configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
variables | Bind test scenario ID, numeric table version number | Used to dynamically specify knowledge base and test context parameters |
| Knowledge Base Selection | Bind the current version's balance test record library | Matches business scenarios where data sources are version feedback and test records |
| Maximum Context | First 1200 characters | Adapts to the typical single-page content length of gameplay design documents and numeric tables |
nodeId | Bind numeric simulation node ID | Used to trigger subsequent processes for defect analysis |
| Single Trigger Limit | Top 5 defect records | Adapts to the volume of feedback data for weekly updates |
| Session Timeout | 600 seconds | Adapts to the typical processing duration of numeric simulation and rule verification |
Three Common Misconfigurations
- Symptom: No optional options appear when referencing variables in the knowledge base search node. Cause: The knowledge base name or version number variable was not predefined in
variables, causing the node to fail to read bound parameters. - Symptom: AI chat node output content mixes workflow return results. Cause: The node was not configured to only execute tasks without output, and the node result was directly mounted to the workflow exit.
- Symptom: Workflow context is lost after multiple rounds of sessions. Cause: Global
variablespersistence configuration was not enabled, causing variables to not be retained across sessions.
How to Confirm Successful Configuration
- Trigger the test workflow, check whether the knowledge base search node automatically loads the specified knowledge base for the current version.
- View the
variablespanel, confirm that bound parameters such as test scenario and numeric version display normally. - Run multiple rounds of test sessions, check whether context parameters are correctly called in subsequent nodes.
- View the workflow logs, confirm that the node bound by
nodeIdtriggers subsequent processes as expected.
The values provided 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.