What the Data for This Category Looks Like
Data for this category mainly comes from CRM systems, product usage data, ticket statistics ledgers, and contract filing ledgers. Data update frequency is typically weekly or monthly. Daily generated documents include customer health reports, renewal forecast tables, high-risk customer lists, and customer communication minutes. Data fields include customer unique identifiers, contract cycle nodes, product usage frequency, ticket response time, renewal progress identifiers, and more. There are no unified fixed units; values adjust based on document type.
Constraints Imposed on Multi-turn Dialogue and Prompt Word Setup
This category’s scattered data sources and low-frequency updates create multiple constraints for multi-turn dialogue and prompt word configuration. First, multi-turn dialogue must bind chatId to the customer unique identifier. This ensures context only links to the corresponding customer’s CRM and product usage data, preventing cross-customer data confusion. Second, data updates occur weekly or monthly. Prompt words must include timeliness verification rules. This stops expired contract ledgers or ticket data from being called. In addition, daily document structures vary. Prompt words must clearly adapt to the field formats of different documents. For example, match corresponding column names when extracting renewal progress identifiers from high-risk customer lists. This reduces manual data interpretation costs. At the same time, errors in this category directly impact customer retention and revenue forecasts. Multi-turn dialogue must include abnormal data verification logic to avoid incorrect data affecting renewal decisions.
The following configurations are based on the functional logic of FastGPT v4.17.0.
How to Set Configuration Values
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId binding rule | Bind customer unique identifier and conversation session ID | Ensure each round of dialogue only associates the corresponding customer's renewal health data, avoiding cross-customer data confusion |
messages context length | Retain the first 8–12 rounds of conversation history | Adapt to weekly or monthly updated data timeliness, filter interference from redundant historical dialogue on current data retrieval |
variables global variable preset | Preset contract expiration date and renewal progress fields | Quickly call standardized business fields, reduce repeated prompt word writing |
| Conversation history isolation setting | Store conversations by customer dimension | Different users can only view their own conversations, ensuring data access permission isolation |
| Conversation record list interface parameters | Set offset step size to 20 | Match batch data paging retrieval for high-risk customer lists, avoiding data omission |
Three Common Misconfigurations
- Phenomenon: In shared public applications, non-current customer users can view other users' conversation history. Cause: The
chatIdis not bound to the customer unique identifier, resulting in session IDs failing to isolate customer data. - Phenomenon: After calling the "Get Conversation Record List" interface, returned AI replies cannot be one-to-one matched with user questions. Cause: The context binding relationship between user questions and AI replies in
messagesis not associated in prompt words, resulting in incorrect data mapping. - Phenomenon: Global variable values display normally when first passed in, but after modifying variables in the same conversation, subsequent replies do not show updated values. Cause: Variable update logic is not written into the
variablescontext synchronization rule, resulting in variable values not being updated in real time during the conversation flow.
How to Verify Correct Configuration
- Initiate cross-customer test conversations using different customer identifiers. Confirm only the current customer's renewal health data is called, with no cross-customer data displayed.
- Call the "Get Conversation Record List" interface. Check that each returned AI reply is correctly associated with the corresponding user question context, ensuring accurate matching.
- Modify the global variable value and initiate a new round of conversation. Confirm the updated variable displays normally in subsequent AI replies.
- Check the permission configuration of the shared application. Confirm different users can only view their own initiated conversation history after logging in, and cannot access other users' conversations.
The values provided here are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.