What This Category of Data Looks Like
This category of data originates from the national medical insurance information platform, hospital HIS and medical insurance interfaces, medical insurance catalog and coding database, and cross-provincial medical settlement platform, with daily updates. Daily processed documents include medical insurance settlement lists, expense details, medical record homepages, prescriptions, rejection lists, and settlement reconciliation statements. Data fields cover settlement order numbers, insured person identifiers, diagnosis and treatment item and drug and consumable codes, expense amounts, rejection reasons, and more. The structure of individual data entries is fixed. The data scale during batch processing adjusts based on settlement batches.
Constraints Imposed by Data Characteristics on Multi-turn Dialogue and Prompt Engineering
The daily update feature of this category of data requires multi-turn dialogue to reference the latest medical insurance catalog and coding library information. This ensures the accuracy and timeliness of matching between audit rules and data. The mixed processing of multiple document types requires prompts to clearly define field boundaries for different documents such as settlement lists, expense details, and rejection lists. This prevents confusion of data dimensions. The fixed structure of individual data entries and variable batch processing scale requires the context window to adapt to associated data of different volumes. This avoids truncation of core audit basis. The rigor of medical insurance audit requires full retention of traceability information during the dialogue process. This prevents missing audit basis due to lost context, which affects the accuracy of case analysis in party building learning.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
messages | Store in groups based on audit scenarios, each group contains 1000–1500 characters of document fragments | The length of single audit document fragments in this category is fixed, and group storage can avoid exceeding dialogue window limits |
variables | Preset medical insurance audit rules and document field mapping variables, update variable values daily | Data in this category is updated daily, and the latest medical insurance catalog and coding rules must be loaded in real time |
chatId | Bind the unique identifier of a single settlement audit, associate all documents of the corresponding batch | This category requires traceability of audit basis by settlement batch, and the unique identifier ensures accurate context association |
| Maximum Context | Retain the first 5–10 historical conversations + 2–4 core settlement documents | The core audit basis of this category is concentrated in a small number of core documents and recent conversations, and excessive content will interfere with audit judgment |
| Conversation Log Retention Period | 90 days | Complies with compliance traceability requirements of medical insurance handling scenarios and meets case review needs in party building learning |
Three Common Misconfigurations
- Phenomenon: Only 2 pieces of historical context are displayed in the conversation details, and replies cannot associate earlier settlement audit cases. Cause:
chatIdis not correctly bound, so each request is recognized as a new session, and context is not properly retained. - Phenomenon: Prompts cannot identify diagnosis and treatment item codes separated by spaces in medical insurance documents, leading to incorrect matching results. Cause: The prompt template does not explicitly require retaining the space format in the document, and no variables are configured to adapt to space-separated fields.
- Phenomenon: Conversation logs are lost, and historical audit learning records cannot be reviewed. Cause: The mount path of conversation logs is not set in the Docker configuration file, or the retention period configuration is lower than the compliance requirement.
How to Verify Proper Configuration
- Initiate a multi-turn conversation containing historical settlement cases, and check whether the number of context items displayed in the conversation details matches the configured maximum context range.
- Input a document fragment containing space-separated diagnosis and treatment item codes, and verify whether the prompt can correctly associate the corresponding audit rules.
- Call the application released via the API to initiate consecutive conversations, confirm that each request carries the correct
chatIdandmessagesparameters. - Check the log retention parameters in the Docker configuration file to confirm that their settings meet compliance traceability requirements.
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.