What the use case data looks like
Data sources for this use case include medical insurance intelligent monitoring systems, fund supervision rule bases, medical insurance settlement data, drug and consumable traceability codes, and complaint clues. Update frequency is monthly. Daily processed documents include audit notices, inspection work papers, suspicious item lists, interview records, handling decision letters, and rectification reports. Data fields include settlement details, flagged suspicious violations, handling basis, rectification status, and others. There is no fixed unified standardized field format. Data dimensions must be adjusted based on the scope of each audit task. Serving entities include medical insurance bureau fund supervision departments, medical insurance handling institutions, flight inspection teams, and designated medical and pharmaceutical institutions.
Constraints on multi-turn dialogue and prompt engineering
The monthly updated data volume is large. Multi-turn dialogue must limit the context window to avoid overflow. Prompts must explicitly restrict calls only to data sources within the current audit cycle. Fields vary significantly across different audit documents, so prompts must adapt to the format requirements of each document to avoid generating ledger content that does not comply with business specifications. Post-event audit processes rely on multi-turn confirmation. Support is required for interrupting current generation and switching tasks. Permission matching for conversation records must also be ensured to prevent unauthorized access to audit data from different entities.
Configuration specifications
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chatId | Exclusive ID for the current audit task | Ensures multi-turn dialogue is bound to a fixed audit task, avoiding data confusion across tasks |
messages | Retain the last 20 turns of dialogue content, with no single turn exceeding 800 characters | Adapts to the volume of monthly data, avoiding context overflow that affects generation accuracy |
variables | Pass in the data source scope and entity type for the current audit cycle | Enables prompts to accurately match the business rules and data sources of this audit |
| Single dialogue timeout | 600 seconds | Ledger summary tasks for monthly audits take a long time, preventing forced mid-task interruption |
| Context window limit | No more than 10,000 characters | Controls context scale to ensure generated content focuses on the current audit task |
Three common misconfigurations
- Phenomenon: After sending a dialogue request, the current model generation process cannot be interrupted by new requests. Cause: No conversation interruption trigger rule is configured, and the generation process under the locked
chatIdruns to completion by default. - Phenomenon: When sending consecutive dialogue requests at short intervals, the later request returns an error stating "No permission to operate this conversation record". Cause: The exclusive
chatIdis not correctly bound, and the system identifies the later request as an unauthorized cross-conversation operation. - Phenomenon: After upgrading to FastGPT v4.17.0, existing records disappear when refreshing the conversation page, and a new conversation is displayed. Cause: The fixed
chatIdis not included in the request, and the system defaults to creating a new conversation session each time.
How to verify proper configuration
- Send two consecutive dialogue requests to the specified
chatId, verify that the later request triggers normally and does not trigger a permission error. - Test interrupting the current generation request, verify that the model stops outputting and can switch to a new conversation task.
- Refresh the conversation page, verify that existing conversation records are not lost, and confirm that the
chatIdbinding is correct. - Trigger ledger content generation, check whether a download link appears at the bottom of the conversation, and adjust the download node configuration according to business needs.
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.