What the data for this category looks like
In marketing scenarios where financial institutions target telecom service users, telecom service business data primarily comes from operator business support systems, customer service ticket platforms, call record databases, and package subscription systems. Data update rhythms fall into two categories: call records and real-time service requests are updated near-real-time, while package tiers and consumption bills are updated daily on a fixed schedule. This documentation uses structured tables as the main format, with core fields including user unique identifier, 11-digit phone number, package type enumeration value, monthly consumption amount (unit: yuan), total call duration in the past 30 days (unit: minutes), complaint ticket status, and more. Some data contains user privacy information and must be processed in accordance with compliance requirements.
What constraints these characteristics impose on the "forms and interactions" link
In financial marketing scenarios, telecom service data fields are numerous and include privacy and real-time requirements. First, forms must support required field validation to avoid null values being passed, which would cause exceptions in subsequent AI calls. Second, real-time data must support dynamic fetching and cannot rely on static caching, otherwise generated marketing content will not match the user's current telecom status, reducing the accuracy of financial marketing. Third, privacy fields must include built-in desensitization rules to prevent sensitive information leaks and comply with financial industry compliance requirements. Fourth, there are many structured fields and enumeration types, so forms must support multiple controls such as dropdown selections and numeric input, while also supporting grouped display based on business scenarios to reduce operational complexity for financial marketing staff.
How to set configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
form_field_required | Set phone number and package type as required, consumption data as optional | Phone number is the user unique identifier, package type is the core classification basis for marketing content. Setting them as required filters invalid inputs |
context_window_size | 8000–12000 characters | Telecom service call records and ticket data have long single entry lengths. Sufficient context allows AI to fully understand the user's business scenario |
dynamic_data_fetch_interval | 30 seconds | Real-time data such as call records updates frequently. Regular fetching ensures the timeliness of interactive content |
form_group_config | Group by basic information, consumption data, and service records | There are many telecom service data fields. Grouping reduces cognitive burden during invocation |
sensitive_data_mask | Replace the middle 4 digits of the phone number with * | Telecom service data contains user privacy information. Desensitization complies with compliance requirements |
ai_trigger_condition | Trigger after the user submits the complete form | Marketing content must be generated based on the user's specific package and consumption status. AI must be called only after all required parameters are passed in |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: The workflow prompts
Cannot convert undefined or null to objectduring operation. Cause: Form required field validation is not configured. When the user fails to fill in core fields such as package type, null values are passed during AI calls, causing a type conversion error. - Phenomenon: AI-generated marketing content does not match the user's current package. Cause: Dynamic data fetching is not set, and old data cached for more than 30 seconds is used, causing recommended content to not match the user's actual status.
- Phenomenon: The second AI node cannot read the output result of the first conversation node. Cause: Context binding parameters are not configured, and the output of the first node is not mapped to the input fields of the second node.
How to confirm the configuration is complete
- Submit a test form, intentionally leave required fields blank, and check if verification prompts for the corresponding fields pop up.
- View data fetch logs to confirm that the dynamic data update interval matches the preset configuration.
- Trigger an AI call, check if the output contains the form field information submitted by the user, and that sensitive information has been desensitized.
- Simulate a multi-turn conversation process to confirm that context is passed normally, and subsequent AI nodes can read the output results of previous nodes.
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.