What the Data for This Category Looks Like
Data sources include marketing content libraries from affiliated financial, insurance, and wealth management sub-brands, customer consultation dialogue history, and customer acquisition and conversion data across business lines. Material updates from each sub-brand are synced daily, and full conversion data refreshes are performed weekly. Document structure is stored categorized by sub-brand, including modules such as material type, applicable scenario, target customer group, and associated conversion metrics. Fields include material ID, release time, reach times, and converted lead volume, with units being items, date, times, and leads respectively.
What Constraints Do These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
Since the data covers layered materials and conversion metrics across multiple sub-brands, multi-turn dialogue must strictly distinguish context boundaries between different sub-brands to avoid mixing cross-brand materials. Daily updated materials require the knowledge base called by prompts to support real-time incremental synchronization, to avoid using expired content. The structured field system requires that multi-turn dialogue accurately match response templates corresponding to target customer groups and conversion metrics, while also specifying the format for field extraction in prompts to ensure consistency of associated data. Cross-business-line customer interaction history must be stored categorized by sub-brand to prevent response adaptation errors caused by context confusion.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Covers multi-turn dialogue history across multiple sub-brands, prevents brand confusion caused by context truncation |
knowledgeBaseRefreshInterval | 1440 minutes | Matches the daily sync of material updates, ensures the latest marketing content is called |
Recall count | Top 8–10 items | Accurately retrieves adapted content for the corresponding brand from multi-sub-brand material libraries, avoids redundancy |
similarityThreshold | 0.75–0.85 | Distinguishes material characteristics of different sub-brands, prevents cross-brand response templates from being incorrectly called |
saveConversationHistory | Enabled | Saves sub-brand identifiers and interaction context for each dialogue, ensures consistency in subsequent conversations |
variableParseTimeout | 30 seconds | Supports dynamic parsing of fields such as platform time variables, prevents missing signatures caused by timeouts |
Three Common Misconfigurations
- Phenomenon: Multi-turn dialogue workflow runs slower than in-workflow debugging. Cause: No reasonable
maxContextvalue is set, and excessive historical context data is loaded on each call. - Phenomenon: Conversation history does not save specified reply content, and automatic replies are empty when re-opening the conversation. Cause: The
saveConversationHistoryconfiguration is not enabled, or themaxConversationHistoryCountvalue is too low, causing context to be cleared. - Phenomenon: Platform time variables in prompts cannot generate correct signature times. Cause: Insufficient
variableParseTimeoutduration is configured, or the dynamic variable call format is not correctly declared in the prompt.
How to Confirm Configuration Is Complete
- Initiate a test dialogue covering multiple sub-brand scenarios, verify that the marketing materials called during the dialogue match the current sub-brand's identifier.
- View the knowledge base sync log, confirm that the material update frequency matches the configured
knowledgeBaseRefreshInterval. - Initiate a test dialogue with a time signature, verify that the signature time generated by the prompt matches the current system time.
- Re-open a saved conversation, verify that historical reply content loads completely with no null values.
The parameter values provided on this page are all common recommended starting points. 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.
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.