Data Characteristics
Medical Information (MI) response tracking data primarily consists of professional replies from pharmaceutical companies' medical departments to medical questions from healthcare professionals and patients. This data is typically in structured or semi-structured text format. It includes fields such as anonymized questioner information, question content, standardized MI department responses, cited literature, response time, and auditor. The data updates frequently, often generated in real-time as the MI department handles daily inquiries. The document structure usually follows internal Standard Operating Procedures (SOPs) and may include key entity information such as disease names, drug names, indications, dosage, and adverse reactions. Field units are typically text descriptions or specific medical measurement units (e.g., mg, ml).
Constraints on Multi-turn Conversation and Prompt Engineering
The highly specialized nature and real-time updates of MI response tracking data impose specific constraints on multi-turn conversation and prompt engineering. First, strict compliance requirements necessitate that prompts guide the model to clearly label information sources and update times when citing tracking data. This prevents the generation of misleading or outdated information. Second, the various entity types (diseases, drugs, dosages) within the data require prompts to precisely guide the model in entity recognition and association, ensuring accurate context transfer in multi-turn conversations. For example, if a user mentions a drug in one turn, the model must accurately associate it with relevant MI response records in subsequent turns. Furthermore, the real-time nature of data updates requires the system to prioritize recalling the latest MI response records in multi-turn conversations to ensure information timeliness.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 10 | Ensures multi-turn conversation context coherence while controlling token consumption. |
Chunk size (Segment Length) | 500–800 characters | Matches the common paragraph length of MI response records, ensuring semantic integrity. |
Recall count (Recall Count) | top 8 | Balances recall relevance with processing efficiency, avoiding irrelevant information interference. |
Similarity threshold (Similarity Threshold) | 0.78 | Ensures high relevance between recalled MI response records and user questions, reducing low-quality recalls. |
Rerank result count (Reranked Return Count) | top 3 | Filters recalled results a second time, prioritizing the most accurate and authoritative MI responses. |
Response Format Prompt | Strictly follow MI response SOPs, cite literature | Forces the model to output MI response formats that meet compliance and professional standards. |
Common Pitfalls
- Issue: AI responses contain outdated or incorrect information, and the source cannot be traced. Reason: Prompts do not explicitly require the model to cite the latest MI records or do not enforce information source labeling.
- Issue: In multi-turn conversations, the AI cannot correctly understand a user's continuous questions about a specific drug or disease. Reason: Prompts do not sufficiently leverage contextual information, causing the model to forget or confuse entities in subsequent turns.
- Issue: When using the API for conversations, the
Humanfield in the response record preview isnull. Reason: ThechatIdor user identity is not correctly passed during API calls, preventing the system from associating historical conversation records.
Verification
- Conduct multi-turn conversation tests to check if AI responses accurately cite the latest MI response records and verify the validity of cited literature.
- Simulate complex multi-turn questions for different diseases and drugs to verify the AI's accuracy in context understanding and entity association.
- Simulate user conversations via the API to check if the system correctly records and displays complete conversation history after passing key parameters like
chatId. - Evaluate the compliance of AI responses, ensuring content adheres to MI department SOP requirements and avoids misleading or unverified statements.
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.