Multi-turn Dialogue and Prompting for Model Allocation Integrated AI Platform

What data does this category include?

This category of data primarily comes from platform model invocation logs, session context records, and model allocation rule configuration files. Update cycles are split into session-level real-time updates and rule-level scheduled synchronization. Session-level data is generated dynamically with each interaction, while rule-level data is synchronized periodically based on business adjustment needs. The document structure includes fields such as model unique identifier, allocation weight, context retention threshold, recall matching rules, and others. Weights are decimals between 0 and 1, session rounds are positive integers, and timeout parameters use seconds as the unit.

What constraints do these characteristics impose in the multi-turn dialogue and prompting link?

Since data is divided into real-time session and scheduled rule categories, model allocation for multi-turn dialogue must be strictly bound to the current session’s context rules to avoid cross-session model allocation conflicts. The setting of the context retention threshold directly limits the injectable content length of prompts. Excessively long contexts will exceed the model’s input upper limit, resulting in truncation of critical business information. Standardization requirements for the model identifier field mandate strict matching of the corresponding format in prompt templates; otherwise, model invocation failures will be triggered. Recall rule configuration affects prompt load. Excessive recall content will cause prompt overload and reduce model response efficiency.

How to configure these settings?

The parameter values provided on this page are all common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to conduct tests on your own samples before finalizing settings.

Three common configuration errors

• Symptom: After setting referenceCount to 20, the large model output does not include recalled knowledge base content. Cause: promptMaxLength was not adjusted synchronously, resulting in recalled content being truncated beyond the prompt upper limit.
• Symptom: Knowledge base paragraph numbers (such as 12356) appear in conversation output. Cause: The prompt template is not configured with field filtering rules, and non-business-related identification information is not blocked.
• Symptom: After deploying a custom model, the model lists displayed in the conversation interface and workspace are inconsistent. Cause: The cache configuration of model allocation rules was not updated synchronously, resulting in different model data being read by different modules.

How to confirm successful configuration

• Initiate a test session with multi-turn interactions, view model invocation logs, and confirm that the allocated model matches the configured modelPriority rules.
• Check the context content in the conversation output to confirm that the retained interaction rounds match the contextRounds configuration.
• View the number of knowledge base paragraphs recalled in the conversation to confirm that it complies with the referenceCount setting requirements.
• Trigger a model switch operation, confirm that the session does not experience timeouts or retry exceptions, and that it matches the modelSwitchRetry configuration.

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.