What the Data for This Category Looks Like
Product consultation data is sourced from official financial institution product manuals, publicly disclosed regulatory term documents, and historical customer service interaction records. Update frequency is adjusted alongside product launches, term revisions, or regulatory requirements, with no fixed cycle. Document structures include fields such as basic product information, underwriting rules, return descriptions, exemption scopes, and common question entries. Field units include RMB yuan, month, year, and other standard units.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
Product consultation data mostly originates from structured regulatory documents and historical interactions. Multi-turn dialogue must strictly output content based on official information, and avoid generating unauthorized content. The non-fixed update cycle requires the dialogue system to support regular knowledge base synchronization to prevent returning outdated product rules. Documents contain multiple segmented fields, so multi-turn dialogue must gradually guide users to clarify product types and consultation scenarios to narrow matching scopes. Fields have clear attached units, so prompt engineering must require replies to use corresponding unit expressions to avoid confusion over core information such as coverage amount and payment cycle.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | Previous 8-10 turns of dialogue context | Product consultation heavily relies on historical interaction product types and user needs; retaining sufficient context avoids repeated inquiries about basic information |
relevanceScoreThreshold | 0.75-0.85 | Product terms are rigorous; a high matching threshold is required to avoid introducing irrelevant rules |
topK | Top 3-5 retrieved knowledge base entries | Core information for product consultation is concentrated; excessive retrieval will interfere with model judgment |
PARSE_FILE_MAX_SIZE | 100 MB | Product documents are mostly regulatory texts; single upload limit adapts to manual volume |
maxToken | 4000-8000 | Product consultation requires referencing multiple clauses; sufficient token length enables complete information integration |
rerankTopK | Top 2-3 entries | Retain the most relevant clause content after reranking to simplify model input context |
The parameter values provided on this page are all common recommendations used as starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three Common Mistakes
- Symptom: An error occurs in dialogue after associating multiple knowledge bases, with no abnormalities in the preview interface. Cause: Cross-region knowledge base synchronization delay in international deployments, or the total character count of associated knowledge bases exceeding the node configuration limit.
- Symptom: The model returns outdated product underwriting rules during multi-turn dialogue. Cause: Updated product documents are not synchronized regularly; the knowledge base still uses old version term content.
- Symptom: Truncation error is triggered when the dialogue input contains a long text JSON file. Cause: Corresponding upload and context parameters are not adjusted, exceeding the system's default character processing limit.
How to Verify Proper Configuration
- Initiate a test dialogue including historical interaction records, and verify whether the model reuses previously mentioned product type information without repeated inquiries about basic content.
- Upload the latest version of the product document, trigger knowledge base synchronization, then initiate a consultation including new rules, and verify whether the model returns updated regulatory content.
- Adjust the similarity threshold and retrieval count, initiate multiple sets of consultations in different scenarios, and verify that the matching degree between returned knowledge base entries and user questions meets expectations.
- Test the long text JSON file upload and parsing process, and verify that the system has no truncation errors and the parsed content is complete and usable.
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.