Multi-turn Conversations and Prompts for Drug Registration and Declaration Document Preparation

Data Characteristics

Drug registration and declaration documents originate from sources such as drug inserts published by the National Medical Products Administration (NMPA), clinical trial data reports, pharmacology and toxicology research reports, authoritative domestic and international medical guidelines, pharmacopoeias, and adverse reaction monitoring data. This data exists in both structured (e.g., clinical trial databases, drug registration databases) and unstructured (e.g., PDF literature, Word document guidelines) formats. Update frequency varies: drug inserts and adverse reaction data are updated irregularly based on post-market surveillance, while medical guidelines have fixed revision cycles, typically ranging from months to several years. Document structures are complex, containing numerous specialized terms, dosage units (e.g., mg, g, IU), administration routes (oral, injection), indications, contraindications, and drug interactions.

Constraints on Multi-turn Conversations and Prompts

The complex structure and specialized terminology of drug information require precise semantic understanding in multi-turn conversation systems. The system must identify and differentiate key information such as drugs, dosages, and indications. Frequent updates to drug inserts mean the knowledge base needs efficient version management and incremental updates to ensure information timeliness. Unstructured documents present challenges for text extraction and structuring, requiring accurate information retrieval. Medical guidelines often include recommendation and evidence levels; the conversation system must understand these hierarchical relationships and reflect them appropriately in responses to avoid misinformation. For coherent multi-turn conversations, the system needs to remember context such as drug names and patient characteristics to provide personalized drug use advice.

Configuration Settings

Common Pitfalls

• Conversation logs show "API call failed: connection timeout." This occurs when the system attempts to call an external database or API for the latest drug information, but network latency or slow service response causes a timeout.
• A user asks about the dosage of a drug for patients with renal impairment, but the response provides a normal dosage. This happens when detailed rules or data regarding medication for special populations are not sufficiently indexed or understood in the knowledge base.
• Drug names or dosage units appear as garbled characters in streaming output. This typically results from an inconsistency between the data source encoding and the system's processing encoding.

Verification

• Conduct multi-turn conversation tests across various typical drug use scenarios. Check for smooth conversation flow and accurate identification and citation of key information (e.g., drug names, dosages, contraindications).
• Randomly select recently updated drug inserts or guidelines from the knowledge base. Ask related questions and verify that system responses align with the latest document content.
• Review system logs for API call timeouts, data parsing errors, or encoding anomalies. Adjust PARSE_FILE_TIMEOUT_SECONDS or data encoding settings as indicated.
• Simulate complex drug interaction questions. Observe if the system can synthesize information from multiple drugs and provide reasonable advice. Evaluate the effectiveness of maxContext and Recall count (Recall Count).

The values provided are common starting points. Measure them against your 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.