Multi-turn Conversations and Prompts for Intent Recognition

Data Characteristics for This Category

In biopharmaceutical private domain consultation conversion, intent recognition data primarily comes from text consultation records within private channels like WeChat, WeChat Work, and in-app messages. This data typically consists of unstructured conversational text. It includes user questions, symptom descriptions, inquiries about medications or treatment plans, and potential product or service needs. Data updates frequently, almost in sync with user consultations. The document structure usually follows a time-series of conversation turns, with each turn containing fields such as sender, receiver, message content, and timestamp. Message content might involve specialized terminology, drug names, and disease descriptions. Some data may include unstructured information like images or speech-to-text conversions.

Constraints Imposed by These Features on "Multi-turn Conversations and Prompts"

High-frequency, unstructured conversational text requires intent recognition models to have real-time processing capabilities and an understanding of colloquial expressions. The nature of multi-turn conversations means a single turn's information is insufficient for complete intent determination; context is necessary for inference. This demands more sophisticated prompt design, guiding the model to focus on conversation history. The presence of specialized terminology and drug names requires the model to possess domain knowledge for semantic understanding, preventing misidentification due to unrecognized terms. Additionally, the privacy of private domain data imposes constraints on data processing and model deployment security, typically requiring operation in localized or private cloud environments. The length and complexity of conversation history directly influence the maxContext parameter setting. A value that is too short can lead to loss of critical information, while a value that is too long can increase inference latency and cost.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale for This Value
`maxContext`	`2000–3000 characters`	Covers common 5–8 turn conversation lengths in biopharmaceutical private domain consultations, retaining sufficient context for intent determination.
`promptTemplate`	Structured template	Provides clear instructions, role-playing, intent classification rules, and reserves `{{history}}` and `{{query}}` placeholders.
`temperature`	`0.3–0.5`	Ensures certainty and stability in intent recognition, avoiding overly divergent responses.
`top_p`	`0.8–0.9`	Balances result diversity and accuracy, allowing the model to explore different expressions within a small range.
`stop_sequences`	`\nUser:`	Prevents the model from generating user-side conversational content, ensuring responses are system-side outputs.
`recall_threshold`	`0.75–0.85`	Ensures recalled knowledge points are highly relevant to user intent, reducing interference from irrelevant information.

Common Mistakes

Slow model response speed, with some conversations experiencing loading timeouts. This occurs when maxContext is set too large, or the prompt contains excessive unnecessary complex instructions, increasing the model's inference burden.
Unstable intent recognition results, where the same type of user query receives different intent labels at different times. This happens when temperature or top_p parameters are set too high, increasing the randomness of model output.
In multi-turn conversations, the model cannot accurately determine user intent, often providing generic responses. This is due to a lack of effective utilization of {{history}} in the prompt, failing to guide the model to infer based on context.

How to Confirm Configuration

Conduct A/B tests to compare the accuracy of intent recognition and user conversion rates with different promptTemplate configurations.
Periodically sample and check model-identified intent labels against manually annotated results, calculating the consistency percentage.
Monitor model response times for different conversation lengths to ensure maxContext performance meets expectations in practical applications.
Analyze user feedback regarding "intent misunderstanding" or "irrelevant responses" as a basis for adjusting temperature and top_p parameters.

The values provided 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 2026-09-21.