What the Data for This Category Looks Like
Data for this category originates from four sources: the national student financial aid management information system, school student registration systems, civil affairs low-income and extremely poor household datasets, and fiscal payment systems. The update cycle runs once per semester. Daily processed documents include family economic hardship certification forms, financial aid application forms, scholarship and grant review forms, disbursement lists, and public notice materials. Fields cover student identity information, family economic status, financial aid application category, review results, disbursement amounts, and bank card information. Cross-check corresponding fields across all documents and systems to ensure data consistency.
Constraints for Multi-turn Dialogue and Prompt Engineering
Cross-system multi-source data cross-check requirements mandate that multi-turn dialogue gradually retrieve field data from different systems. This prevents context overflow. Semester-based batch-updated ledger data requires prompts to support batch summary logic. Prompts cannot only process single entries. Multi-document field matching requirements mandate that prompts explicitly specify field combinations to align. This avoids missed check items. The correspondence between public notice materials and disbursement lists requires multi-turn dialogue to guide alignment of student lists and amount information across the two document types. This reduces risks of incorrect or missed disbursements.
Configuration Settings
This configuration uses FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum Context Window | 8000–12000 token | Adapts to semester batch ledger summary and report generation needs, avoids truncation of critical check information |
| Conversation Timeout | 600 seconds | Adapts to batch data processing duration needs, avoids request interruptions due to large data volume |
| Question Optimization | Enabled | Splits user check requests and guides gradual retrieval of field data from different systems |
| Global Variable Injection | Configure fixed variables per semester batch | Unifies prompt field matching logic, adapts to format differences across cross-system data |
| Conversation Context Binding | Bind the unique identifier of the semester financial aid batch | Distinguishes ledger data from different semesters, avoids cross-batch context confusion |
| Output Format Validation | Enforce closed tag rules | Prevents unclosed quotation mark garbled code during output |
Three Common Misconfigurations
- Symptom: AI dialogue returns content that does not match the query, with answers to unrelated questions. Cause: Dialogue context is not isolated per semester batch, leading to confusion between financial aid data from different batches.
- Symptom: AI dialogue node returns an empty response. Cause: The prompt does not explicitly specify field combinations to match, or global variables do not inject necessary system mapping rules, preventing the large language model from generating valid content.
- Symptom: Unclosed quotation mark garbled code appears at the end of AI output. Cause: The prompt does not explicitly specify output format closed rules, or context truncation leads to incomplete format markers.
How to Verify Correct Configuration
- Import a single entry of student financial aid data, trigger a multi-turn dialogue, and verify that returned check results include cross-matched information about family economic status and student registration status.
- Review conversation logs to confirm that the batch ID bound to conversation context is correctly passed with each request, and no confusion occurs between batches from different semesters.
- Inject global variables, trigger a prompt call, and verify that output content includes preset field mapping rules.
- Simulate a batch ledger summary request, and confirm that returned report format meets field requirements of public notice materials.
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.