What Data for This Category Looks Like
Data for this category primarily comes from search ad backends, search term reports, bidding ranking data, and landing page data. Most updates occur on a daily or weekly basis. Daily generated documents include standardized keyword lists, negative keyword tables, bid records, and search term reports. Each document contains fields such as keyword text, match type, bid amount, click volume, conversion count, and more. There are no unified fixed units, and adjustments must align with the field rules of the corresponding advertising platform. Primary service stakeholders include SEM optimizers, agency operations staff, and brand marketing personnel. Daily work focuses on keyword expansion, screening, and adjustment.
What Constraints Do These Characteristics Impose on the Workflow Orchestration Link
This page is written based on FastGPT v4.17.0. Data sources are diverse. Update frequencies differ between daily and weekly cycles. Workflows must be configured with switchable trigger timing to adapt to the pull rhythm of different data sources. Daily document fields must be adjusted for different advertising platforms. Workflows must use parameter mapping to adapt to different field rules. This prevents abnormal data flow. Core time-consuming links focus on keyword expansion, negative keyword screening, and bid adjustment. Workflows must be split into independent sub-nodes. This facilitates separate debugging and reuse. Errors may lead to increased invalid clicks, brand term hijacking, and other negative outcomes. Workflows must be configured with failure handling and alert nodes. This intercepts abnormal operations in a timely manner.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Trigger Timing | Set according to data source update frequency (daily/weekly) | Match actual data update rhythm to avoid invalid pulls or delayed processing |
| Field Mapping Rules | Configure variables according to advertising platform field names | Adapt to field differences across search ad backends and ensure normal data flow |
| Failure Alert Threshold | 10% abnormal data share | Timely intercept abnormal configurations that may cause increased invalid clicks |
| Node Splitting Granularity | Split into keyword pull, negative keyword screening, and bid adjustment sub-nodes | Correspond to core time-consuming links, facilitating separate debugging and maintenance |
| Timeout Limit | 600 seconds | Adapt to average time consumption of multi-data-source pulls and LLM processing |
Three Common Mistakes
- Workflows return empty fields after being called. The cause is incorrect configuration of
variablesmapping to advertising platform field names, resulting in failed normal data flow. - Content misalignment occurs when processing long-text keyword lists. The cause is failure to split lists into appropriately sized batches, leading to LLM's inability to accurately match corresponding text and operation instructions.
- Knowledge base search works correctly during debugging but fails during conversation calls. The cause is failure to configure
nodeIdbinding to the specified knowledge base node in the workflow, resulting in inability to associate preset knowledge base resources during calls.
How to Confirm Proper Configuration
- Manually trigger the workflow, check the running logs of each node, and confirm that target documents such as keyword lists and negative keyword tables are pulled correctly.
- Verify field mapping configurations, view the returned data structure through test nodes, and confirm that fields mapped via
variablesmatch the return fields of the target platform. - Simulate an abnormal data scenario, trigger the failure alert rule, and confirm that the alert logic activates normally.
- Run the workflow multiple times, check whether the average time consumption matches the preset timeout limit, and adjust the timeout setting as needed.
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.