What this category of data looks like
Typical data for this category comes from central enterprise internal control systems, ERP systems, financial sharing systems, audit management systems, and procurement contract project systems, with an annual update frequency. Daily documents include internal control self-evaluation reports, audit notices, audit working papers, issue lists, rectification ledgers, and rectification closing certificates. Each document corresponds to different links in the business process, and fields include issue number, rectification responsible person, rectification deadline, supporting material links, and more. Cross-system data must be associated via unified business identifiers.
What constraints these characteristics impose on the "multi-turn dialogue and prompt engineering" link
Annually updated documents have stable structures but inconsistent field formats across systems. This requires multi-turn dialogue to accurately associate document data from different sources via unified business identifiers, to avoid mixing content across projects. Large-volume documents such as audit working papers and rectification ledgers require the dialogue system to support long document chunking, to prevent context overflow caused by overly long single chunks. The strongly associated attribute of issues and rectification in audit processes requires prompts to clearly guide the dialogue to extract and match corresponding business fields, ensuring responses only relate to content from the current audit project. The business attribute with severe consequences of errors requires the multi-turn dialogue context to periodically truncate expired content, to avoid interference from redundant data.
How to set the configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk length | 800–1200 characters | Adapt to the business unit length of documents such as internal control self-evaluation reports and audit working papers, retain complete issue or rectification item information |
| Number of recalled documents per request | Top 5 entries | Audit issues are mostly associated with a small number of related documents; excessive recall will disrupt dialogue logic, focus on core business content |
messages parameter format | Arranged in the order of system, user, assistant | Clarify the business boundaries of multi-turn dialogue, ensure prompts prioritize compliance rules for audit rectification |
variables parameter configuration | Bind current audit project ID and rectification status field | Accurately associate corresponding business data, avoid cross-project confusion, meet the association requirements of cross-system data |
| Conversation history truncation threshold | Automatically truncate after 600 seconds of no interaction | Annually updated documents do not require long-term retention of expired conversation contexts, reduce redundant resource usage |
| Maximum context window | 16000 token | Adapt to the total content length of audit issues and rectification ledgers in multi-turn dialogue, prevent context overflow |
Three common mistakes
- The symptom is redundant document title content returned in the conversation log during API calls. The cause is that the
variablesparameter is not correctly configured to filter non-business-related fields, resulting in irrelevant information from document metadata being returned. - The symptom is duplicate audit issue entries appearing in multi-turn dialogue. The cause is that no conversation history truncation threshold is set, causing the context window to accumulate too much old issue list data, disrupting current dialogue logic.
- The symptom is excessively long parsing time for Word format documents over 10MB. The cause is that the chunk length is not adjusted to fit the long document structure, resulting in too many chunks and increased parsing load.
How to confirm the configuration is correct
- Initiate a test conversation containing a specified audit issue number, verify that the returned content only includes the rectification requirements and supporting materials for the corresponding issue, with no redundant metadata.
- Initiate a continuous multi-turn conversation, wait beyond the set truncation threshold, verify that old conversation history is no longer included in the current context.
- Upload a 10MB+ Word format audit working paper, verify that the parsing time meets expectations.
- Call the conversation interface associated with
chatId, verify that the returnedmessagesparameter only contains valid business conversation content, with no irrelevant fields.
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.