What data for this category includes
Data originates from business systems of central state-owned enterprise internal control departments, audit departments, finance departments, subordinate enterprises, and external audit institutions. It covers documents such as internal control self-evaluation reports, audit notices, audit working papers, issue lists, rectification ledgers, and rectification closing certificates. Data sources include enterprise internal control systems, ERP systems, financial sharing systems, audit management systems, and procurement contract project systems. The update frequency for this data is annual. During daily processing, extracting business samples and vouchers, compiling audit issues and rectification requirements, and tracking rectification evidence and closing steps take up a large portion of total processing time.
Constraints for multi-turn dialogue and prompt engineering
The annual update frequency means multi-turn dialogue contexts do not need frequent refreshes of full historical data. The annual archived dataset can be called consistently. Document structures include content with clear associated fields, such as issue lists and rectification ledgers. Multi-turn dialogue must accurately match the previous round’s questions and corresponding rectification steps. This prevents interference from irrelevant cross-year data. Data sources are spread across multiple systems. Prompt engineering must explicitly specify the data source scope. This stops cross-category data from being included in processing. The time-intensive nature of sample voucher extraction and similar steps requires caching historical conversation conclusions in multi-turn dialogue. This reduces repeated calls to underlying data.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Generate a unique identifier per audit business batch | Ensures conversational context for the same audit rectification batch remains consistent, and prevents mixing of data across batches |
messages | Retain the most recent 15–20 conversation records | Aligns with the multi-turn communication logic of audit workflows, covering key stages such as issue submission, rectification feedback, and closing confirmation |
variables | Bind only the dataset exclusive to the current application | Limits data scope to category-specific documents including internal control self-evaluation reports and audit working papers, preventing interference from irrelevant data |
| Maximum context length | 8000–12000 characters | Matches the total character count of annual archived documents, avoiding truncation of critical rectification ledger and issue list content |
| Historical data refresh frequency | Synchronize daily at midnight | Matches the low-frequency annual update characteristic, eliminating the need for high-frequency underlying data refreshes |
Three Common Misconfigurations
- Phenomenon: Conversation history retains records from non-current business batches, and cannot perform bulk cleanup for the specified application. Cause: No unique
chatIdis generated per business batch. The system retains all conversation records by default, leading to mixing of cross-batch data. - Phenomenon: Multi-turn dialogue cannot use the previous round’s audit issue conclusion as input for the next round’s prompt. Core information must be entered repeatedly. Cause: The
messageshistorical message retention configuration is not enabled, and the previous round’s conclusion is not bound to the current session variables. - Phenomenon: The completion reason field of the conversation component cannot be referenced by subsequent components, preventing linked generation of rectification ledgers. Cause: The completion reason in
messagesis not written to session variables, so subsequent components cannot read the corresponding field.
How to Verify Correct Configuration
- Initiate a multi-turn conversation containing audit issues and rectification requirements. Check that the context only retains interaction content from the current session.
- Access the
variablesconfiguration panel. Confirm that only category-specific datasets such as internal control self-evaluation reports and audit working papers are bound. - Generate a unique
chatIdto start a new conversation. Confirm that no other batch’s audit data appears in the historical records. - Test using the previous round’s audit issue conclusion as a precondition for a new question. Check that the conversation continues based on that conclusion.
The values provided here are common starting points, and should be evaluated against the relevant organization’s own specific 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.