What data for this category looks like
Insurance investment research data primarily comes from registered insurance product terms, annual actuarial reports, regulatory documents from the National Financial Regulatory Administration, industry association statistical reports, and public industry claims datasets. Product term documents have fixed structures, including fields such as insured age range, annual premium amount, maximum coverage amount, and scope of exclusions. Actuarial reports include structured content such as investment asset categories and reserve provision items. Data updates follow product registration, regulatory policy release, and annual financial report disclosure timelines, with no fixed weekly update cycle.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
The decentralized update timeline of insurance investment research data requires multi-turn dialogue contexts to link to the latest registered products and regulatory documents, to avoid retrieving outdated information. Structured document fields and specific unit requirements mean prompts must clearly define field matching rules for recalled content and unit consistency. In multi-product comparison scenarios, multi-turn dialogue must retain previously mentioned product names, scope of liability, and other context to reduce repeated retrieval. Additionally, the scope restrictions of public industry datasets require prompts to strictly limit recalled content to publicly available information, and prohibit calling unauthorized individual customer data.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxHistoryTurns | Last 4–6 turns | Insurance investment research conversations mostly focus on product comparisons and policy associations. 4–6 turns can retain core context while controlling token consumption, avoiding interference from redundant information |
similarityThreshold | 0.72–0.85 | Insurance product terms contain many similar phrasing. A threshold that is too low will introduce irrelevant retrievals, while a threshold that is too high will miss matching detailed liability clauses |
recallTopK | Top 8–12 results | Insurance investment research data includes structured tables and long texts. 8–12 results can cover multi-dimensional retrieved content such as product liabilities and actuarial data |
promptTemplate | Fixed template, must include "Only use knowledge base content to respond, do not introduce external information, and responses must match units and field names in the documents" | Insurance investment research has high requirements for data accuracy. It is necessary to strictly limit the retrieval scope and output format to avoid expressions that do not comply with regulatory requirements |
rerankTopN | Top 3–5 results | A large number of similar entries exist in insurance data. Reranking can filter low-correlation retrieval results and focus on core product terms and regulatory documents |
maxTokenPerChunk | 800–1200 characters | The core paragraphs of insurance terms and actuarial reports mostly fall within the 800–1200 character range, ensuring that a single segment contains complete liability or data items |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test against your own samples before finalizing.
Three common misconfigurations
- Phenomenon: Reply content includes unauthorized information outside the knowledge base. Cause: The prompt template does not explicitly require only using knowledge base retrieved content, or the configuration does not link to the permission scope of the corresponding knowledge base.
- Phenomenon: Inconsistent dialogue context and retrieval rules across different application scenarios. Cause: The
maxHistoryTurnsandrecallTopKparameters are not unified, or templates in different workspaces are not kept synchronized. - Phenomenon: Deviations appear in multi-turn dialogue analysis results, missing cross-turn associated information. Cause:
maxHistoryTurnsis incorrectly set to 1, only retaining the most recent conversation content, which does not cover the context requirements of cross-product comparison in insurance investment research.
How to confirm the configuration is correct
- Initiate a test conversation involving multi-round insurance product comparisons, and verify that the reply only uses information within the knowledge base and does not introduce external content.
- Check the number of retained dialogue context turns, and confirm that it matches the configured
maxHistoryTurnsvalue. - Verify the number and relevance of retrieved results, confirming that they comply with the configured retrieval and reranking rules.
- Upload a single long document for parsing, confirming that the segmented content is complete and no core fields are truncated.
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.