Multi-turn Conversations and Prompts for Lead

Lead Data Characteristics

Biopharmaceutical lead data originates from online promotions, offline exhibition registrations, academic conference sign-ups, and partner referrals. This data is typically structured or semi-structured. Update frequency varies by source; for example, online advertising leads may enter the system in real-time, while exhibition leads might be imported weekly or monthly. Common document structures include CRM customer records, Excel spreadsheets, or JSON files. Key fields include Name, Contact Number, Intended Product/Service, Organization, Position, Region, Lead Source, First Contact Time, and Follow-up Status. The Intended Product field may contain specific drug names, medical device models, or clinical trial service types, usually as text descriptions.

Constraints on Multi-turn Conversations and Prompts

Diverse lead data sources and varying update frequencies require the multi-turn conversation system to synchronize the latest information promptly. This avoids information lag that could lead to conversations inconsistent with the lead's actual status. The mix of structured and semi-structured data means prompt design must accommodate the extraction and understanding of different fields. For example, the system must recognize standardized Intended Product lists and process non-standard product descriptions provided verbally by users. The Follow-up Status field necessitates conversation flow control. The system must adjust conversation strategies and recommended content based on the lead's lifecycle stage. For new leads, the focus is on product introduction and needs discovery. For leads already followed up, the system must query historical records to provide more personalized consultations. The biopharmaceutical field uses specialized terminology; prompts must identify and process these terms to ensure accurate information delivery.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`8` turns	Balances memory capacity and computational overhead, covering most lead consultation scenarios.
`temperature`	`0.3–0.5`	Ensures accuracy and consistency of responses, reducing the risk of generating irrelevant information.
`top_p`	`0.7–0.8`	Balances response diversity and focus, avoiding overly divergent or rigid outputs.
`recall_top_k`	`5–7` items	Improves the accuracy of recalling relevant lead information from the knowledge base, reducing redundancy.
`similarity_threshold`	`0.75–0.85`	Precisely matches user queries with lead knowledge, preventing misleading information.
`prompt_template`	Calibrate by measurement	Optimizes understanding and generation for biopharmaceutical terminology and lead fields.

Common Pitfalls

Information in the conversation contradicts the lead status, such as recommending products to an already signed client. This occurs due to delayed synchronization or missing processing logic for the lead's Follow-up Status field.
The system fails to provide effective responses or gives overly generic replies when users ask about specific drug names. This happens because the knowledge base lacks sufficient coverage of biopharmaceutical specialized terminology, or prompts do not effectively guide the model to use specific knowledge.
The conversation's opening statement does not offer quick question options, leaving users unsure how to start a consultation. This is due to not configuring a Quick Questions list in the system's Opening Statement Settings.

Configuration Validation

Simulate conversations across various lead statuses (new lead, followed-up, closed deal) to ensure system responses align logically with the Follow-up Status field.
Ask questions using biopharmaceutical specialized terminology and product names. Check if the system accurately identifies and provides relevant information. Evaluate the effectiveness of Recall count (recalltopk) and Similarity threshold (similarity_threshold) for the knowledge base.
Verify if the opening statement includes guiding quick questions. Confirm that users can initiate predefined consultations smoothly after clicking, and that the maxContext parameter effectively maintains multi-turn conversation context.
Analyze logs to observe the model's token consumption and response time when processing lead data. This evaluates the overall performance of parameters like temperature and top_p.

The values provided are common starting points and should be measured against specific 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.