What this category of data looks like
The marketing content data for this sector primarily comes from internal marketing management systems, offline branch promotional material libraries, and online official channel content repositories. The data update rhythm is adjusted per individual marketing campaign cycles. During the campaign cycle, script details are fine-tuned daily. After the campaign concludes, data is automatically archived to the historical repository. Most documents are structured tables and text blocks with compliance tags. Fields include campaign ID, applicable customer group tiers, script templates, compliance verification tags, and effective time periods. Units are based on characters, customer group tiers, and effective time periods.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
Structured fields require multi-turn dialogue to guide users to confirm fields in sequential order to avoid missing required items. Prompt engineering must explicitly embed field mapping rules to prevent unstructured parsing errors. Dynamically updated marketing content requires multi-turn dialogue configurations to call real-time recall interfaces, ensuring the latest effective campaign content is always used during sessions. It is necessary to verify whether the campaign associated with the current session is within the compliant effective time period. The presence of customer group tier fields requires prompt engineering to clearly define output rules, ensuring scripts are accurately matched for different customer groups. Compliance verification logic must also be included in prompt engineering to prevent output of content that violates financial regulatory requirements.
How to Set Configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000-12000 characters | Adapts to scenarios where marketing content for this sector includes long campaign details and multi-round customer group confirmation, preserving complete conversation context |
promptTemplate | {context}, please generate regulatory-compliant marketing scripts combined with compliance verification tags, and output according to customer group tiers | Embeds compliance verification and structured output requirements to meet the compliance business needs of this sector |
RECALL_TOP_N | Top 3-5 entries | Balances content matching accuracy and context redundancy, avoiding output confusion caused by excessive recall |
SIMILARITY_THRESHOLD | 0.75-0.85 | Accurately matches compliant marketing content while covering eligible campaign templates |
STREAM_RESPONSE | Enabled | Streaming output of long marketing content reduces user waiting perception, aligning with financial service interaction habits |
WORKFLOW_VAR_SYNC | Real-time sync | Ensures real-time access to the latest marketing campaign data during multi-turn dialogue, avoiding use of expired content |
The parameter values provided on this page are all common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to perform testing on relevant samples before finalizing settings.
Three Common Misconfigurations
- Issue: Context truncation persists in multi-turn dialogue after configuring
maxContext. Cause: ThePARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted synchronously, leading to incomplete parsing of long documents and failure to fully load conversation context. - Issue: The online dialogue interface returns empty data with a 400 status code. Cause: No reference to the compliance verification field was added in
promptTemplate, resulting in parameter verification failure. - Issue: In version v4.8.10, AI responses to multi-turn dialogue are one-time outputs without streaming output. Cause: The
STREAM_RESPONSEconfiguration item was not enabled, or the service was not restarted after configuration to take effect.
How to Confirm Successful Configuration
- Test multi-turn dialogue, input questions for different customer group tiers, and verify whether output content includes compliance verification tags for corresponding fields.
- Call the online dialogue interface to check whether returned results include complete marketing campaign content, with no empty fields or truncation.
- View workflow logs to confirm that variables output by code runs have been synchronized to context parameters of AI dialogue nodes.
- Adjust
SIMILARITY_THRESHOLD, then test matching accuracy of recalled content to ensure it meets business requirements.
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.