Multi-turn Dialogue and Prompt Engineering for Pre-existing

What the Use Case Data Looks Like

Data sources for pre-existing condition determination include medical insurance settlement details, outpatient/inpatient medical records, physical examination reports, and archived health disclosure submissions from insurance applications. Data is pulled in real time for the associated insured person when a claim is submitted, with no fixed periodic updates. Document structures vary across sources:

Medical insurance settlement details include fields for visit date, hospital name, diagnosis name, and cost breakdown
Medical records include fields for chief complaint, present medical history, pre-existing medical history, and medical orders
Physical examination reports include fields for various metrics and annotated abnormal items

Diagnostic coding follows the ICD-10 standard uniformly. Visit dates use the YYYY-MM-DD format. Costs are denominated in yuan.

Constraints for Multi-turn Dialogue and Prompt Engineering

Dispersed data sources and multimodal content (structured tables, unstructured text) impose key requirements:

Multi-turn dialogue must organize information sequentially by data source type, to avoid context window overflow from overly long single inputs
Data is pulled in real time per claim, so associated data sources must be updated synchronously during each dialogue call. Static cached content cannot be relied on
Fields have standard coding requirements. Prompts must explicitly specify ICD-10 code identification, and cannot rely solely on text descriptions, to ensure standardized determination results
Document lengths vary widely. Long medical records may consume significant context space. Reasonable segmentation and recall rules must be configured to prevent critical information truncation

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`8000–12000 characters`	Pre-existing condition related data includes multiple medical records and settlement details, with a total length typically between 6000-10000 characters. Reserve sufficient context to avoid truncation of critical information
`recallTopK`	`Top 8 entries`	Pre-existing condition data is scattered across different visit records. Too many recalled entries introduce redundant information, while too few risk missing critical pre-existing medical history
`promptTemplate`	`Organize pre-existing condition information in the order of "visit records - medical insurance settlements - health disclosures", identify ICD-10 codes, and combine with the visit records of the current claim application to determine whether it falls under the pre-existing condition category`	Clarify the organization order to avoid information confusion, specify coding standards to improve determination accuracy, and tie to the claim application context to ensure targeted determination
`enableMCP`	`Enabled`	Real-time pulling of latest settlement data via medical insurance interfaces is required. Static knowledge base historical content alone should not be relied on
`similarityThreshold`	`Calibrated via actual testing`	Adjustments must be made based on the pre-existing condition association rules of the business scenario, to filter low-relevance historical visit records
`timeout`	`300 seconds`	Cross-data-source pulling and multi-turn verification require lengthy processing times, to avoid interrupting the determination process due to timeout

The parameter values provided on this page are common starting points for configuration setup. Actual values are influenced by material format, data volume, and business rules. Each scenario requires individual analysis. It is recommended to test on your own samples before finalizing settings.

Three Common Misconfigurations

Symptom: The newContext and aiReply fields output by multi-turn dialogue nodes have duplicate content, and valid pre-existing condition data is not retained. Cause: The boundary between context splicing and reply generation is not distinguished, and AI reply content is incorrectly mixed into the context queue.
Symptom: After configuring enableMCP to call the medical insurance data interface, the returned pre-existing condition determination result does not match the actual records. Cause: The prompt does not explicitly require prioritizing structured data returned by MCP for determination, and still relies on static knowledge base content.
Symptom: After a claim application is submitted, the AI reply content is irrelevant to the current query, and incorrectly associates pre-existing condition records of other insured persons. Cause: The global variable applicantId is not correctly bound in the workflow, resulting in data sources calling incorrect insured person information.

How to Verify Proper Configuration

Trigger a simulated claim application, check the spliced content of the context field, and confirm that valid data arranged in the order of visit records, medical insurance settlements, and health disclosures is included.
Call the MCP tool to pull test medical insurance data, check whether the returned results include preset ICD-10 codes and visit information, to confirm that the tool is functioning correctly.
Submit a test case with known pre-existing conditions, verify whether the AI's returned determination result matches the preset pre-existing condition records, and adjust similarityThreshold to a range that meets business requirements.
Check the binding configuration of global variables in the workflow, confirm that variables such as policyId and applicantId have been correctly passed into the prompt template, and no null values are present.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.