What this category of data looks like
Data for this category comes from enterprise internal control systems, ERP systems, financial shared service systems, audit management systems, and procurement contract project systems. The update frequency is annual. Teams process these documents daily: internal control self-assessment reports, audit notices, audit working papers, issue lists, rectification ledgers, and rectification closure certificates. Most document structures contain fields including business sample extraction rules, issue qualification standards, rectification time limit requirements, and supporting material lists. There is no fixed unified unit. Field requirements from the corresponding business system must be matched.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Data sources are scattered and mostly stock data from business systems. Multi-turn dialogue must clearly associate field permissions of corresponding systems to avoid unauthorized access. For low-frequency data updated annually, fix the data range in prompts to avoid introducing expired information. Diverse document types and qualitative content require multi-turn dialogue to gradually clarify audit scenarios and issue dimensions, and refine prompt parameters. The most time-consuming sample extraction link requires guiding users to provide sampling rules through multi-turn dialogue to reduce manual intervention.
How to configure
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the information density per chunk of documents such as audit working papers and rectification ledgers, avoiding information fragmentation within chunks |
| Overlap Length | 100–200 characters | Retains cross-chunk business association information, such as cross-chunk rectification progress descriptions in rectification ledgers |
| Max Context | First 10 dialogue turns | Aligns with the dialogue logic of state-owned enterprise and public sector audit scenarios, avoiding excessive historical information interfering with current question processing |
| Single Dialogue Turn Limit | 15 turns | Meets the multi-step communication needs of the audit rectification process, covering the full dialogue flow from sampling to closure |
| Prompt Template | Fixed association with business system fields | Ensures generated documents match the standard formats of internal control and audit systems |
| Variable Call Scope | Only call audit management system data under the current account | Complies with data permission requirements, avoiding cross-account data leakage |
Three Common Misconfigurations
- Phenomenon: Empty fields are returned when calling the
variablesparameter to obtain prompt content under a specified account. Cause: The variable call scope is not configured, or the data interface of the corresponding business system is not authorized for access. - Phenomenon: No response is provided after multi-turn dialogue exceeds the set number of turns. Cause: The number of historical
messagesentries is not restricted, leading to context overflow. - Phenomenon: Generated rectification documents do not reference specified audit working paper content. Cause: The prompt does not clearly specify the associated document source field, or the chunk length is set too large, causing key information to be truncated.
How to Verify Proper Configuration
- Initiate a test dialogue request that includes
chatId, and confirm that the returned dialogue context contains preset historical messages. - Upload an audit working paper, trigger multi-turn dialogue, and check whether the generated issue list matches the content in the paper.
- Adjust the single dialogue turn limit parameter, and test whether the system automatically truncates historical messages after exceeding the set number of turns.
- View the configured variable call scope, and confirm that it only includes audit management system data under the current account.
The values provided here are common starting points, and should be evaluated 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.