What Data for This Category Looks Like
This category's data originates from enterprise internal control systems, ERP systems, financial sharing systems, audit management systems, and procurement contract project systems. The update frequency is annual. Daily documents include internal control self-assessment reports, audit notices, audit working papers, issue lists, rectification ledgers, and rectification closing certificates. Most documents combine structured fields and unstructured text. Fields cover issue number, rectification responsible entity, completion deadline, supporting material association items, and similar items. There is no unified fixed unit; field units vary based on the specific document type.
What Constraints These Characteristics Impose on the "Multi-turn Dialogue and Prompt Engineering" Link
Data sources for this category are scattered, and the update frequency is low. Multi-turn dialogue must associate context information from different systems to ensure accurate alignment between policy basis, audit issues, and rectification requirements. Documents contain both structured fields and unstructured text, so prompt engineering must adapt to both field extraction and text understanding scenarios. Since the data update frequency is annual, knowledge base synchronization does not require high-frequency triggers, but full coverage of all documents is required after updates. Audit-related documents need strict traceability; multi-turn dialogue must retain context linked to original document numbers and source systems to avoid basis deviations caused by lost context.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum context length | 8000–12000 token | Covers all annually updated audit documents and multi-turn dialogue history, and adapts to the context processing limit of FastGPT v4.17.0 |
chatId binding rule | Associate with audit project number | Ensures context association for multi-turn dialogue under the same project, and avoids cross-project confusion |
messages context retention duration | 30 days | Matches the typical span of audit rectification cycles, and meets context requirements for cross-day dialogue |
| Single search limit | Top 8 entries | Covers main categories of audit documents, and ensures comprehensive retrieval of policy basis |
variables preset fields | Bind issue number and rectification responsible entity | Adapts to field extraction for structured documents, and enhances precise matching for multi-turn dialogue |
| Question optimization switch | Enabled | Simplifies vague follow-up questions in multi-turn dialogue, and improves the accuracy of policy basis retrieval |
Three Common Misconfigurations
- Phenomenon: The conversation log is empty after invoking the workflow. Cause: The association rule between
chatIdand workflow input parameters is not configured, leading to incorrect context transfer. - Phenomenon: Policy basis in multi-turn dialogue does not match the current audit project. Cause:
chatIdis not bound to the audit project number, and cross-project context is not isolated, leading to retrieval of irrelevant documents. - Phenomenon: Conversation logs are lost within a short period. Cause: The
messagescontext retention duration is set too short, or conversation log persistence configuration is not enabled, leading to automatic cleanup of session data.
How to Confirm the Configuration Is Correct
- Launch a multi-turn dialogue targeting a single audit project, and check if the context of each dialogue round is associated with the document number of the current project.
- View the conversation log list, and confirm that the
chatIdfield displays the bound audit project number, with no cross-project mixing. - Trigger a knowledge base update, then launch a historical follow-up question, and check if updated document content can be retrieved correctly.
- Wait for the preset
messagescontext retention duration, then re-enter the session, and confirm that the conversation history can still load normally.
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 2026-09-28.