What Data for This Category Looks Like
This category’s data typically comes from search ad backends, search term reports, bidding ranking data, and landing page data. Update cycles are mostly daily or weekly. Regularly produced documents include keyword lists, negative keyword tables, bid records, and search term reports. Fields across all documents include keyword text, matching mode, bid amount, impressions, clicks, conversion data, and more. Some documents must be linked to the delivery time window and region settings of their corresponding accounts.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
This category’s data is divided by delivery account, region, and time window, with multiple independent document types. Multi-turn dialogue must clearly distinguish different task contexts to avoid data confusion. Data updates occur daily or weekly. The dialogue flow must support regular synchronization of the latest search term reports and bidding data to ensure the timeliness of information used in prompt calls. Core tasks fall into three categories: keyword expansion, negative keyword screening, and bid matching adjustment. Multi-turn dialogue must split interactions by task nodes, retain operation context for each step to facilitate review and adjustment. Operation logs from different optimizers must be stored independently. Identification parameters must be used to distinguish dialogue ownership.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Generate a unique identifier using delivery account + task type | Distinguish dialogue histories for different accounts and tasks, avoid context confusion |
messages | Split rounds by task node, pass document fragments related to the current task in each round | Adapt to multiple independent document structures, reduce context overload |
variables | Preset file paths or data IDs for keyword lists and negative keyword tables | Quickly access category-specific business data, reduce repeated input costs |
| Single document length per request | 800–1200 characters | Match the content length of individual search term report entries, improve prompt understanding efficiency |
| Maximum context length | 8000–12000 token | Match the total content volume of category documents, avoid exceeding model processing limits |
| Dialogue retention duration | 7 days | Adapt to weekly data update cycles, regularly clean expired data |
| FastGPT version | v4.17.0 | Adapt to parameter passing and log storage logic for this version |
Three Common Misconfigurations
- Passing
chatIdvia API yields no expected results, and corresponding dialogue ownership is not displayed in backend logs. This occurs because a unique identifier is not generated using delivery account and task type, or the parameter passing format does not meet requirements. - Users accessing via guest links have their backend logs deleted after deleting dialogue content. This occurs because front-end dialogue status and backend log storage logic are not separated, and the same storage path is directly bound.
- New dialogues cannot load previously saved global variables. This occurs because the
variablesparameter is not used to bind the unique data ID of global variables, making it impossible for dialogue context to link to historically stored business data.
How to Verify Configurations Are Correct
- Initiate a new dialogue and pass
chatId, check if the corresponding identifier is displayed in backend logs to confirm normal context association. - Upload category-specific keyword lists and negative keyword tables, call them via the
variablesparameter, confirm that the dialogue can directly read the corresponding data. - Initiate multi-turn interactions, check the dialogue history after each operation, confirm that context is split by task nodes, and no irrelevant data is mixed.
- Test the dialogue deletion operation via guest links, confirm that backend logs are not deleted synchronously, and storage logic meets expectations.
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.