What the data for this category looks like
Personnel policy Q&A data comes from official documents released by internal compliance departments of central state-owned enterprises and public service institutions. These include specialized handbooks covering attendance and leave, compensation management, recruitment and probation, performance appraisal, and more. Documents follow a chapter and clause structure, with fixed fields including policy name, effective date, applicable audience, implementation details, and additional relevant content. Effective dates use the YYYY-MM-DD format. Applicable audiences are divided into ranges based on job sequence and years of service. Update cycles vary with policy adjustments, with no fixed schedule. Individual document lengths range from several thousand to tens of thousands of characters.
Constraints imposed by these characteristics on multi-turn dialogue and prompt engineering
Personnel policy Q&A data has a strict structure tied to clear applicable scenarios. Updates follow no fixed cycle, and document lengths vary significantly. These characteristics require that multi-turn dialogue accurately associate clause numbers, to avoid content deviations caused by fuzzy matching. Regular synchronization of the latest policy content is required, to prevent expired clauses from being referenced in conversation context. Supplementary information about applicable audience attributes such as job position and years of service must be collected, to narrow the scope of policy matching. Context length must be controlled, to avoid exceeding the model input limit, and ensure the accuracy and stability of multi-turn interactions.
Configuration Settings
The following configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Generate a unique identifier per session | Used to bind conversation context, ensuring session consistency for multi-turn interactions |
messages | Retain the most recent 10–15 rounds of conversation content | Personnel policy documents are lengthy. Excessive context will exceed the model input limit. Retaining recent interactions allows accurate association with current questions |
variables | Inject the current user's job position and years of service fields | Match clauses in policies targeted at specific groups, narrowing the search scope |
| Maximum context length | 8000–12000 token | Adapt to the input limits of general large models, avoiding truncation of critical policy clauses |
| Question optimization | Enable automatic follow-up to supplement information | Prompt for supplementary key details such as job position and entry time for scenarios that are not clearly specified |
| Session expiration time | 7200 seconds | Ensure sessions are retained for a reasonable duration, avoiding expired context interfering with current interactions |
Three Common Misconfigurations
- Symptom: The AI dialogue node returns an input length limit exceeded error, or directly returns a no-result response. Cause: No context length limit is configured, and historical content accumulated from multi-turn dialogue exceeds the model input limit.
- Symptom: Specific initiating users cannot be associated in conversation logs. Cause: The
chatIdparameter is not configured in the workflow, making it impossible to bind the unique identifier of the user session. - Symptom: Returned policy clauses do not match the user's actual applicable scenario. Cause: Key information such as the user's job position and years of service is not injected via
variables, making accurate matching of corresponding clauses impossible.
How to Verify Configurations Are Correct
- Initiate multi-turn questions that include job position and years of service, check whether the AI can accurately associate corresponding policy clauses.
- Review conversation logs, confirm that each session carries a unique
chatIdidentifier, allowing targeted user conversations to be located via the identifier. - Simulate input of historical conversations that exceed the preset context length, check whether the system triggers length limit interception or automatic truncation.
- Test questions that do not provide key scenario information, check whether the AI automatically follows up to supplement necessary content.
The values provided 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.