What This Category of Data Looks Like
Coverage liability data is primarily sourced from insurance contract clauses, insurance application disclosure documents, and related certification materials submitted with claim applications. The update schedule for this data is tied to insurance contract activation and claim submission. Initial loading completes when a contract activates. The latest contract version is linked when a claim is submitted. Most documents are structured clause text, including core fields such as coverage item names, payout limits (unit: yuan), deductible ratios, and excluded liability scopes. Some additional insurance coverage liabilities are split into sub-clauses.
Constraints Imposed on Multi-turn Dialogue and Prompting
The structured split nature of coverage liability clauses requires multi-turn dialogue to guide users through confirmation step-by-step by clause hierarchy. This avoids loading all clauses at once, which would exhaust token quotas. The data tied to contract versions requires dialogue context to carry contract version identifiers. This ensures the called coverage liability data matches the current claim application. Some fields include numerical values with units. Prompts must explicitly require extracted results to retain corresponding units. This prevents generation of ambiguous payout amounts without units. The presence of additional insurance sub-clauses requires dialogue flows to support retrieving corresponding coverage liability information by insurance type classification.
Configuration Settings
| Configuration Key | Recommended Value | Rationale |
|---|---|---|
maxContext | Previous 4 historical dialogues + 1 contract clause snippet (≤1200 characters) | Coverage liability clauses are compact after splitting. Excess historical dialogue will consume token quotas, while retaining interaction context for claim preliminary review |
temperature | 0.1–0.3 | Coverage liability review requires strict matching to clause content. A lower temperature prevents generation of ambiguous explanations that deviate from clauses |
Recall count | Top 3 matching coverage liability clauses | Coverage liability entries for a single insurance contract are limited. Excessive recall will cause information redundancy and reduce review efficiency |
Similarity threshold | 0.75–0.85 | Professional phrasing is common in coverage liability clauses. A higher similarity threshold ensures matched clauses are accurately linked to the current claim application |
max_tokens | 800–1200 | Responses for coverage liability review must include clause references, payout judgments, and explanations. Excessively long token limits will lead to redundant or truncated responses |
PARSE_FILE_TIMEOUT_SECONDS | 60 seconds | Most coverage liability clause documents are structured text, with short parsing times. A timeout setting prevents invalid waiting |
The parameter values listed on this page are general recommendations used as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Each scenario requires separate analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Dialogue token statistics displayed in the interface only include main input content, and do not include uploaded contract clause documents. This occurs because some configurations do not enable the document token statistics switch, leading to incomplete display of token consumption for this interaction.
- Prompt configurations do not explicitly define a time range, making it impossible to extract coverage liability trigger records from the current year or month. This occurs because prompts do not add explicit constraints on the time dimension, so the model cannot accurately match claim scenarios within the specified period.
- Setting
maxContext=0in the workflow prevents dialogue from associating historical coverage liability query records for claim applications. This occurs when the context window is set to 0, so the system does not pass previous dialogue history, preventing the model from reusing prior coverage liability matching results.
How to Verify Proper Configuration
- Check token consumption statistics in dialogue logs to confirm that the statistical scope of input and output tokens includes contract clauses and interaction content.
- Submit a test claim application with explicit time constraints, and verify that the coverage liability judgment returned by the model matches the specified time period.
- Adjust the
maxContextparameter, then test the context association effect of consecutive multi-turn dialogues to confirm that historical interaction information is correctly called. - Check context configuration items in the workflow to confirm that the values match the context requirements of the dialogue flow.
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.