What the data for this category looks like
Data for this category originates from the national student aid management information system, school student status system, civil affairs low-income and special hardship dataset, and fiscal payment system. The update cycle runs once per semester. Daily processed documents include family economic hardship certification forms, aid application forms, scholarship and grant review forms, distribution lists, and public notice materials. Each document includes fields such as student ID number, family economic status, bank card information, and review results. The data structure is fixed, and this data is only used for student aid qualification verification and document generation related to fund distribution.
What constraints these characteristics impose on the multi-turn dialogue and prompt engineering link
Multi-source data sources require multi-turn dialogue to integrate field verification logic from different systems, to avoid mismatched cross-system data. The per-semester update cycle requires prompt engineering to limit processing to current semester application data, to avoid using expired student status or low-income information. Documents contain sensitive fields, requiring multi-turn dialogue to include privacy protection rules in prompt engineering, to avoid leaking sensitive content such as bank card information. The rigor of the review process requires each dialogue turn to verify whether extracted fields comply with business rules, to avoid missing or incorrect fields.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
variables | Pass structured student information in the format {"student_id": "", "family_economic_status": "", "bank_card": ""} | Review documents for this category require extracting fields such as student ID number, family economic status, and bank card information. Passing structured data via variables avoids parsing errors from unstructured content |
messages | Retain the most recent 8 turns of dialogue | The review process for this category includes multi-turn interactions such as application, verification, and supplementary material submission. Retaining the most recent 8 turns of dialogue ensures context coherence, and avoids repeatedly asking for already submitted application information |
chatId | Bind the unique identifier of the current review batch | Reviews for this category are processed per semester batch. Binding all dialogue and data from the same batch via chatId ensures multi-turn dialogue belongs to the same review process, and avoids cross-batch confusion |
| Question Optimization | Enable | User input for this category may include vague descriptions of family economic status. Enabling question optimization converts vague statements into standardized field extraction requests, which aligns with document field matching rules |
| Maximum Context Length | 800–1200 characters | This category has a large number of document fields. Setting a context length of 800–1200 characters covers key information from multi-turn dialogue, and avoids context overflow |
| Single Search Limit | Top 3 results | Civil affairs low-income data for this category requires matching the top 3 most relevant records, to avoid excessive irrelevant data interfering with reviews |
Three Common Mistakes
- Phenomenon: After passing
variablesvia API, the generated aid application form does not include the passed student ID number field, and the returned content is unrelated to the passed variables. Cause: The pending verification forvariablesis not embedded in the prompt engineering, so the passed structured data is not called. - Phenomenon: During multi-turn dialogue, fields extracted from the family economic hardship certification form are empty. After triggering a specified reply, the system does not wait for the user to supplement information, and the dialogue ends directly. Cause: No node waiting for user input is configured in the workflow, and the dialogue process is not retained after extraction fails.
- Phenomenon: The English prompt engineering is configured to require English documents, but the generated public notice materials only use Chinese. Cause: The output language is not clearly specified in
messages, or the model's output language rules are not limited.
How to Confirm Configuration is Complete
- Call the API, print the passed
variablescontent, and check whether the passed fields match the placeholders in the prompt engineering. - Start multi-turn dialogue, input a vague description of family economic status, and check whether the system triggers a standardized extraction request after question optimization.
- Trigger the text extraction link, input content that does not include required fields, and check whether the system waits for the user to supplement information.
- After configuring English prompt engineering, check whether the language of the generated documents meets the requirements.
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.