What this category of data looks like
Typical data sources for this category include central enterprise internal control systems, ERP systems, financial sharing systems, audit management systems, and procurement contract project systems. Daily used documents include internal control self-assessment reports, audit notices, audit working papers, issue lists, rectification ledgers, and rectification closure certificates. Data is updated annually. Core time-consuming links involve extracting business samples and vouchers, forming audit issues and rectification requirements, and tracking rectification evidence and closure.
What constraints do these characteristics impose on the multi-turn dialogue and prompting link
For annually updated data, multi-turn dialogue must bind the exclusive context of a fixed audit project to avoid mixing cross-project data. For differences in data structures across multiple source systems, prompts must clearly distinguish field rules for different documents, guiding the dialogue to gradually obtain voucher information from corresponding systems. For the process characteristics of core time-consuming links, multi-turn dialogue must guide interaction in the order of "extract samples - confirm issues - track rectification" to avoid requesting too much information at once. For the severity of potential errors, prompts must verify the matching between input data and historical ledgers, preventing repeated submission of similar audit issues.
How to set up the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum context length | 8000–12000 characters | Must carry multiple annual updated audit documents and rectification ledgers to avoid context overflow |
variables | Bind audit subject and rectification ledger number | Accurately associate internal control data and exclusive issue lists of the corresponding enterprise |
messages | Retain the last 15 dialogue records | Support multi-turn confirmation links for rectification tracking, avoiding loss of historical evidence requirements |
| Timeout setting | 600 seconds | Adapt to retrieval time across multiple system data sources, avoiding Request Time errors |
| Question optimization | Enabled, calibrated based on actual testing | Convert vague rectification requirements into executable voucher extraction instructions |
chatId | Bind audit project ID | Distinguish dialogue contexts for different audit projects to avoid data mixing |
Three common misconfigurations
- Phenomenon: Rectification suggestions returned by the dialogue do not match the historical issue list. Cause: Failed to bind
variablesto associate the project’s issue ledger, leading to incorrect data being called in the context. - Phenomenon: Request Time errors occur during local deployment and operation. Cause: Did not configure a reasonable timeout parameter, or failed to correctly associate multiple system data sources, resulting in excessive retrieval time.
- Phenomenon: The interface displays English text after iframe embedding. Cause: Did not specify Chinese interaction language in the configuration, or failed to correctly pass localization parameters.
How to confirm the configuration is complete
- Initiate a query containing the audit project number, check whether the returned content is associated with the rectification ledger fields of the corresponding project.
- Initiate multiple rounds of rectification tracking queries consecutively, check whether the dialogue context retains key information from historical interactions.
- View the variable binding items in the configuration interface, confirm that exclusive fields such as audit subject and rectification ledger have been associated.
- Test the iframe embedding scenario, check whether the interface language meets the preset requirements.
The values provided here 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 2026-09-28.