Data profile for this category
The data for this category mainly comes from ad delivery platforms, internal material libraries, user interaction data, and conversion data. Daily produced documents include creative scripts, material test sheets, delivery data reports, and iteration checklists. Most documents are updated daily or weekly. Data fields include creative material type, copy content, delivery time slot, click volume, conversion cost, and more. There are no unified fixed units; units adjust based on different document types. Information feed optimizers, creative teams, and growth teams maintain data daily. The core test variable is the combination of materials and copy, which differs significantly from keyword management for search ads.
What constraints these characteristics impose on multi-turn dialogue and prompt workflows
Multi-turn dialogue must support context synchronization across data sources to handle decentralized data sources and inconsistent update frequencies. Prompts must adapt to document inputs in varying formats. Diverse daily document structures require prompts to have flexible field recognition capabilities, which can automatically adapt to different document types such as creative scripts and test sheets. Multi-turn dialogue must limit context length to avoid response delays from excessive historical data, as data updates occur frequently. The core test variable is the combination of materials and copy. Prompts must focus on comparative analysis of this combination to avoid interference from redundant information. Errors affect delivery effectiveness. Multi-turn dialogue must ensure accurate test conclusion outputs. Prompts must include guidance for compliance verification.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Generate a unique identifier per creative test batch | Isolate conversation contexts for different tests to avoid data cross-talk |
messages | Retain the most recent 15 historical conversation messages | Adapt to fast-paced test iteration rhythms, control total context length |
variables | Bind three core variables: material type, copy variant, delivery time slot | Dynamically pass test parameters to support multi-round comparative analysis |
| Response timeout threshold | 30 seconds | Match the time range of daily document processing, avoid long wait times |
| Maximum context token count | 6000–8000 token | Balance context completeness and response speed, adapt to existing document scale |
| Authorization verification rule | Bind project-specific API key | Restrict access permissions, ensure test data only circulates within authorized teams |
This configuration applies to FastGPT v4.17.0.
Three Common Misconfigurations
- Symptom: An unauthorized error is prompted when calling integrated plugins. Cause: The API key of the conversation application is not fully bound to the plugin configuration, or the permission scope of the API key does not cover plugin call permissions.
- Symptom: Unable to switch AI models in the conversation interface. Cause: The multi-model switching configuration item is not enabled, or multiple authorized available models are not configured for the conversation application.
- Symptom: Test conclusion fields are missing from multi-turn dialogue responses. Cause: Core test variables such as material type and copy variant are not bound in
variables, causing prompts to fail to obtain valid input data.
How to Verify Proper Configuration
- Initiate a creative test conversation request, check that the conversation is bound to the
chatIdof the current test batch, to ensure contexts are not confused with other tests. - View the model list in the configuration items, confirm that the required AI models have been added and can be switched normally.
- Call the bound integrated plugin, input test-related parameters, confirm that no authorization errors occur and returned data meets expectations.
- Enable streaming output configuration, initiate a conversation, confirm that response content is pushed segment by segment gradually.
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.