Data Characteristics
Core data for e-commerce pharmaceutical platforms include product information for medications, medical devices, and health supplements. It also includes pharmacist consultation records, user health profiles, and medication feedback. Product information typically originates from pharmaceutical companies and distributors, provided in instruction manuals and product handbooks. This data updates frequently, covering batch numbers, expiration dates, inventory, and price changes.
Document structures are primarily structured data, such as drug attributes (generic name, brand name, dosage form, specification, manufacturer, approval number), indications, dosage and administration, contraindications, and adverse reactions. Unstructured data includes user consultation texts, reviews, and detailed descriptions for some products. Field units are clearly defined, such as dosage units (mg, ml), specification units (tablet, piece), and packaging units (box, bottle).
Constraints Imposed by Data Characteristics on Multi-turn Conversations and Prompts
The structured nature of e-commerce pharmaceutical data allows for precise extraction of user intent in multi-turn conversations, matching it to specific product attributes. For example, if a user mentions a specific drug's dosage form or specification, the system can directly link to the corresponding field. High update frequency requires continuous knowledge base synchronization to ensure accurate pricing and inventory information, preventing outdated responses. The standardized nature of professional documents, such as drug instruction manuals, means prompt design must emphasize accuracy and rigor in medical terminology, ensuring professional replies. Concurrently, the unstructured parts of user consultations, such as symptom descriptions and medication experiences, demand stronger semantic understanding from the multi-turn conversation system. It must extract key information from vague descriptions and guide users to provide clearer details.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 8 | Balances context length and retrieval efficiency, covering common multi-turn consultation scenarios. |
Chunk Size | 500 characters | Adapts to the paragraph structure of professional texts like drug instructions, ensuring semantic completeness. |
Recall Count | 7 | Enhances knowledge coverage for multi-turn conversations while ensuring relevance. |
Similarity Threshold | 0.78 | The pharmaceutical domain demands high information accuracy, avoiding interference from low-relevance content. |
Rerank Return Count | 3 | Selects the most relevant information, reducing user reading burden and improving response accuracy. |
prompt for Drug Attribute Extraction | Generic Name, Brand Name, Dosage Form, Specification, Indication, Dosage and Administration | Guides the model to prioritize core drug information, improving query accuracy. |
Common Pitfalls
- The chatbot provides inaccurate or outdated drug information. This occurs when the knowledge base synchronization mechanism is incomplete, failing to update the latest product batches, prices, or delisted items in a timely manner.
- After a user inputs a drug name, the chatbot fails to recognize it or gives irrelevant responses. This manifests as the model outputting a
422error code or returning empty fields. This can be due to insufficient coverage of drug aliases or synonyms in the knowledge base, or a tokenization strategy not optimized for medical terminology. - In a multi-turn conversation, the user mentions symptoms, but the chatbot fails to effectively link them to corresponding drugs or suggestions, leading to conversation breakdown. This occurs when the prompt inadequately guides the model on symptom-to-drug association rules, failing to effectively leverage knowledge graphs or medical diagnostic knowledge.
Validation Steps
- Select a representative set of drug query and consultation scenarios. Manually simulate multi-turn conversations to verify the accuracy and timeliness of the chatbot's responses.
- Periodically update the knowledge base with newly listed or price-changed drug information. Test whether the chatbot can provide the latest data within a short time frame.
- Randomly select real user consultation records. Use playback testing to evaluate the chatbot's understanding of complex medical terms and vague symptom descriptions.
- Design adversarial test cases for easily confused drug names or similar symptoms. Observe whether the chatbot can differentiate and provide correct advice.
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.