What data is included in this category
Data for this category originates from enterprise internal control systems, ERP systems, financial sharing systems, audit management systems, and procurement contract project systems. Data updates annually. Daily processed documents include internal control self-assessment reports, audit notices, audit working papers, issue lists, rectification ledgers, and rectification closure certificates. Most document fields cover responsible entities, rectification requirements, supporting material numbers, and similar details. Common units are item, copy, and person-time. This data centers on problem discovery and rectification closure. Core relationships tie to audit scope and rectification progress corresponding to enterprise hierarchy levels.
What constraints do these characteristics impose on the multi-turn dialogue and prompt words link
Annual updated datasets require dialogue contexts to bind to fixed audit project cycles, eliminating frequent refreshes of historical data. Differences in fields across multiple types of heterogeneous documents require prompt words to clearly specify extraction rules for corresponding documents, to avoid confusing field meanings across different material types. The decentralized nature of data sources requires multi-turn dialogue to gradually guide users to provide supporting materials from corresponding systems, and gradually improve context completeness. The core goal of rectification closure requires dialogue sessions to track rectification status and closure progress, ensuring interaction content focuses on problem rectification. Additionally, chatId binding must associate with audit project IDs, and variables must preset business rules to prevent dialogue from straying from the core scope of internal control audit.
How to configure settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind to the current audit project ID | Associate case and declaration material context for the corresponding project, to avoid mixing content across projects |
| Maximum context length | 8000–12000 characters | Cover core content of a single audit working paper and rectification ledger, to avoid truncating critical information |
| Single search limit | Top 6 entries | Match the common number of supporting materials in audit files, to avoid interference from redundant information |
variables | Preset audit project type, rectification status fields | Inject fixed business rules into dialogue context, reducing repeated input of business parameters |
messages | Retain the most recent 10 turns of dialogue content | Track multi-turn interactions for rectification follow-up, ensuring context is coherent and not overloaded |
| System prompt | Only process declaration materials and file review related to internal control evaluation, audit and rectification; extract fields for issues, rectification requirements, and closure certificates | Limit dialogue scope to avoid straying from business objectives |
Three common configuration errors
- Phenomenon: Dialogue references knowledge base content that does not match the current audit file, with irrelevant financial reimbursement voucher information appearing. Cause: No context association rules are configured, causing the called knowledge base to not be limited to audit data for the current project.
- Phenomenon: Rectification suggestions output by the dialogue exceed the preset field scope, including unmentioned non-audit related content. Cause: The system prompt does not clearly define the business scope, with residual prompt logic from generic configurations.
- Phenomenon: Response delay occurs after more than 10 turns of multi-turn dialogue. Cause: The number of dialogue turns retained in
messagesis not limited, causing the context volume to exceed the processing threshold of FastGPT v4.17.0.
How to confirm configuration is complete
- Initiate a test dialogue, input a fragment of an audit working paper, and verify whether the AI reply accurately extracts the preset issue and rectification requirement fields.
- Switch between different audit projects, check whether
chatIdbinding takes effect, and ensure that contexts are not mixed across projects. - Initiate a multi-turn dialogue, gradually supplement rectification ledger and closure certificate information, and verify whether preset fields in
variablesare correctly injected into the interaction context. - View system logs to confirm that the retained dialogue content in
messagescomplies with the preset turn limit.
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.