What the data for this category looks like
Unlike keyword management for search ads, the core test objects for this category are the combined performance of images, videos and copy. Data mainly comes from ad delivery platforms, internal material libraries, user interaction data and backend conversion data. Update frequencies are mostly daily or weekly. Daily associated documents include creative scripts, material test sheets, delivery data reports and iteration checklists. Data fields include material format, copy content, delivery targeting, click-through rate, conversion cost and more. There are no unified fixed field length requirements. Configurations must match the current ad account settings.
What constraints these characteristics impose on workflow orchestration
Data sources are scattered and update frequencies vary. Workflows must support connecting to multi-source data interfaces, while adapting to field differences across different ad accounts. Daily documents have low structural consistency. Workflows must include text parsing nodes to convert content from scripts and test sheets into processable test cases. The material fatigue judgment and test conclusion summarization links rely on real-time interaction data. Workflows need scheduled triggering and batch processing capabilities to avoid overall process disruption from single-node timeouts. Errors directly impact delivery effectiveness. Workflows must add failure retry and alert nodes to ensure timely response to exceptions.
How to set configurations
These configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Global variable initialization | Configure variables according to the test case list, including material ID, copy template, delivery targeting | Match the fields in daily creative scripts and test sheets |
| Multi-source data pull node | Set the pull time range to the last 7 days, with a maximum of 200 entries pulled per request | Adapt to daily or weekly updated data frequencies and avoid interface overload |
| Node triggering rule | Trigger according to the test case ID marked in variables | Precisely match the independent test process for each creative |
| Failure retry configuration | Set retry count to 3 times, with a 60-second retry interval | Address temporary fluctuations and short-term exceptions in ad platform interfaces |
| Global variable acquisition timing | Read assigned variables when the button is triggered | Avoid retrieving uninitialized variable values |
| Workflow export | Export configuration files in node execution order | Facilitate version management and cross-team synchronization |
Three common misconfigurations
- Attempting to add custom parameters to existing components without them taking effect. The cause is failing to pre-register the parameter's key name and type in the workflow's
variables, so the system does not load the custom configuration item. - Receiving unassigned global variables when clicking the data panel button. The cause is that the variable assignment node executes after the button trigger node, so variables have not completed initialization.
- Triggering a risky behavior error during workflow debugging. The cause is that the node is configured with a loop logic without termination conditions, triggering the system's built-in security interception mechanism.
How to confirm the configuration is complete
- Execute one test case and check whether the global variable list includes all preset
variablesparameter values. - Trigger the data panel button and verify that the returned variable values match the real-time data in the current workflow.
- Simulate an ad platform interface exception and check whether the workflow triggers the preset retry and alert logic.
- Export the workflow configuration file and verify that the node order and parameter configurations match the editing interface.
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.