What this category of data looks like
Data sources for this category include medical insurance intelligent monitoring systems, fund supervision rule bases, medical insurance settlement data, drug and consumable traceability codes, and complaint clues. The update frequency is monthly. Commonly processed documents include audit notices, inspection work papers, suspicious item lists, interview records, decision letters, and rectification reports. Document fields mostly involve medical insurance settlement codes, expense amounts, violation judgment bases, and similar content. Units include yuan, person-times, drug batches, and similar units. A single audit file often contains multiple associated documents.
Constraints for Multi-turn Dialogue and Prompt Engineering
Monthly updated rule bases and settlement data require dialogue contexts to link to the latest version of supervision rules. This avoids referencing expired judgment standards. Multiple structured and semi-structured documents require prompt engineering adapted to different document field extraction logic. For example, extract inspected entity information from audit notices, and locate violation entries from suspicious item lists. A single file contains multiple associated documents. This requires limiting the context length per dialogue turn to prevent exceeding model processing limits. It also needs to support multi-turn recall to link documents from different stages. For example, combine complaint clues and settlement data to verify suspicious items. This ensures the coherence of audit logic.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum Context | First 12000 characters | Adapts to the length requirements of splicing multiple documents in a single audit file, avoids exceeding model processing boundaries |
| Question Optimization | Enabled, use medical insurance supervision terminology template | Adapts to professional terminology, improves the accuracy of violation suspicious item identification |
| Single Search Limit | First 6 entries | Matches the typical number of suspicious entries in a single audit file, filters redundant search results |
variables | Bind monthly updated supervision rule variables | Ensures that dialogues always call the latest version of fund supervision rules |
chatId | Bind unique identifier of a single audit file | Achieves context consistency for multi-turn dialogue within the same audit file |
messages | Limit single-turn dialogue history to no more than 20 turns | Controls total context length, prevents response timeouts |
Three Common Misconfigurations
- Phenomenon: An error occurs after input guidance is triggered in the debug preview interface. Cause: The custom thesaurus for input guidance is not configured with medical insurance supervision professional term mapping, causing the model to fail to recognize compliant fields.
- Phenomenon: The maximum response token for some questions automatically changes to 200, which is far below the configured 3000 limit. Cause: The dialogue context contains historical messages exceeding the model's supported length, triggering the built-in context truncation logic, leaving insufficient token quota.
- Phenomenon: The question optimization step takes too long, with single-turn optimization exceeding 10 seconds. Cause: No professional term optimization template is configured. The model needs to additionally process non-general content such as medical insurance settlement codes and violation judgment rules, increasing inference time.
How to Verify Successful Configuration
- Launch a multi-turn dialogue for a single audit file, check that the unique identifier bound to
chatIdremains consistent in the dialogue history. - Trigger question optimization, check that the optimized prompt contains matching rules for medical insurance supervision professional terms.
- Import monthly updated supervision rule data, check that the
variablesautomatically link to the latest version of rule content. - After configuring the response token limit, launch a query containing violation judgment content, check that the response length matches the expected configuration.
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.