What the data for this use case looks like
Marketing-related data for this use case primarily comes from in-house retail business systems, user interaction records from online and offline channels, and supporting data from phased localized marketing campaigns. Data sources include user consultation logs from mobile banking and official WeChat accounts, customer communication ledgers from offline branches, and in-house customer group tagging libraries. Update frequency follows three schedules: customer group tagging data is synchronized daily, real-time interaction records are stored immediately upon conversation completion, and marketing campaign-related data is updated in batches according to the campaign cycle. Fields for a single marketing conversation data entry include customer unique identifier, conversation initiation time, interaction channel, conversation turn, associated activity ID, user input content, and AI reply content. Conversation turn is a positive integer, interaction duration is measured in seconds, and customer identifier is a string of 16 to 20 characters.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
The binding of customer unique identifier and conversation turn in marketing conversation data requires that multi-turn dialogue context must be strictly associated with a single user ID to avoid cross-user context confusion. Context windows must also be expanded step by step according to conversation turn to prevent redundant information from interfering with core intent recognition. The requirement for immediate storage of real-time interaction records means that log storage for conversation nodes must support low-latency writing, and context refresh frequency must match the real-time nature of user interaction. The associated activity ID field requires that prompt templates dynamically embed exclusive information for the current activity, and prompt templates for different activities must support quick switching. Differences in interaction channels require the multi-turn dialogue module to adapt to input formats of different channels: offline scenarios must support context processing after long-text speech transcription, and online scenarios must optimize intent recognition efficiency for short text.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Marketing conversations for this use case typically include 3 to 5 turns of interaction, plus associated marketing activity information. This range covers complete context without exceeding the input limits of mainstream large models |
CONTEXT_BIND_USER_ID | Enabled | This use case requires strict association of single-user conversation data to avoid cross-user context confusion, which aligns with customer privacy management requirements |
UPLOAD_FILE_MAX_SIZE | 5 MB | Image files for marketing materials in this use case typically do not exceed 5 MB. This setting covers common material upload requirements |
MAX_INPUT_LENGTH | 2000 characters | Blocks overly long user input to avoid triggering errors from large model input limits, and adapts to text lengths from offline speech transcription |
CHAT_LOG_DELETE_ENABLE | Configured by role permissions | This use case requires control over conversation record deletion operations based on job permissions, which complies with data security and compliance audit requirements |
LOG_STORAGE_RETENTION_DAYS | 180 days | Complies with financial industry regulatory requirements for retaining user interaction logs, and supports customer group analysis and compliance backtracking |
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 targeted analysis, and it is recommended to test on your own samples before finalizing.
Three Common Misconfigurations
- Phenomenon: After a user inputs overly long text, the conversation node returns a
413 Request Entity Too Largeerror, and invalid input is not intercepted in advance. Cause: TheMAX_INPUT_LENGTHparameter is not configured, or its value does not adapt to long text scenarios from offline branch speech transcription. - Phenomenon: Conversation contexts from different users are incorrectly shared, and conversation logs do not carry valid customer unique identifiers. Cause: The
CONTEXT_BIND_USER_IDconfiguration is not enabled, or the customer ID field is not bound in the workflow. - Phenomenon: When uploading JPG marketing activity materials, the system prompts that the file size exceeds the limit and cannot complete content parsing. Cause: The
UPLOAD_FILE_MAX_SIZEconfiguration value is smaller than the file size of commonly used marketing materials for this use case.
How to Verify Proper Configuration
- Initiate a simulated user conversation, check whether the conversation log carries the customer unique identifier field, and confirm that context is not shared across users.
- Upload a JPG material matching the common size for this use case, verify that the file upload and content parsing process works correctly.
- Input a test text that exceeds the preset length, verify that the system intercepts the input in advance and prompts that the input has exceeded the limit.
- Check log storage and permission configurations, confirm that retention rules and deletion permissions comply with financial industry compliance 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.