What this category's data looks like
Daily data for medical insurance fund supervision and audit includes two categories: standardized official documents and system-structured data. Standardized documents include audit notices, inspection work drafts, suspicious item lists, interview records, handling decision letters, and rectification reports. Their fields include suspicious item ID, involved institution information, violation description, and more. Structured data sources include the medical insurance intelligent monitoring system, fund supervision rule base, medical insurance settlement data, drug and consumable traceability codes, and report clues. The update frequency is monthly. The rule base contains standardized violation judgment clauses. Settlement data includes fields such as medical treatment subject, expense details, and medical insurance settlement identification.
What constraints do these characteristics impose on the multi-turn dialogue and prompting link
Standardized documents require multi-turn dialogue to strictly link to the exclusive ID of the current audit task, to avoid mixing audit content from different batches. Monthly updated data sources require prompts to consistently reference the latest version of the rule base, and outdated content must not be used. Multi-source structured data has high information density. The messages parameter must limit the context length to prevent exceeding the model's processing limit. Audit results are directly linked to fund losses and administrative penalties. Prompts must clearly specify that the model only generates responses based on provided documents and data, and external information must not be introduced.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind to the suspicious item list ID of the current audit task | Ensure multi-turn dialogue context only links to the current audit task, avoiding cross-task information interference |
messages | Retain the first 10 rounds of dialogue context, truncate within the 8000–12000 token limit | Balance context completeness and model response speed, adapting to the information density of multi-turn dialogue for medical insurance audit |
variables | Inject the jurisdiction rule base ID of the current audit institution and the involved institution code of the current suspicious item | Dynamically call supervision rules for the corresponding jurisdiction, adapting to audit requirements of different regions |
| Question Optimization | Enable | Medical insurance policy terms are highly professional. Convert colloquial questions into standard audit terms to improve retrieval matching accuracy |
| Maximum Context | 8000–12000 token | Avoid model deviation caused by overly long context, adapting to the model parameter limit of FastGPT v4.17.0 |
Three Common Misconfigurations
- Phenomenon: Content returned after calling the knowledge base does not match the current question. Cause: The exclusive rule base of the current audit task was not injected via
variables, causing the model to call general knowledge base content. - Phenomenon: The AI dialogue node returns an empty response with the error
chat:LLM_model_response_empty. Cause: No context truncation rule was configured for themessagesparameter, causing the input content to exceed the model's processing limit. - Phenomenon: Unclosed reference symbol garbled characters appear at the end of AI output. Cause: No output format was specified in the prompt, causing special symbols in standardized documents to be incorrectly carried over.
How to Verify Correct Configuration
- Check if
chatIdmatches the suspicious item list ID of the current audit task, to confirm correct context association. - Test inputting a colloquial question, check if it is automatically converted to standard medical insurance audit terms, to confirm the question optimization configuration is active.
- Simulate multi-turn dialogue, input audit questions from different links in sequence, check if the model retains previous suspicious item information, to confirm the
messagesparameter configuration is reasonable. - Trigger an empty response test scenario, check if the context truncation rule is triggered, to confirm model response stability.
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.