Data Characteristics
Medical affairs regulatory documents originate from pharmaceutical company internal rules, industry regulation interpretations, pharmaceutical professional guidelines, and clinical research reports. These documents update quarterly or annually, driven by policy adjustments, new drug approvals, and clinical practice guideline revisions. Urgent policies may release in real-time. Document structures feature clear hierarchical chapters and clauses. They often contain extensive technical terms, acronyms, and legal citations. Fields frequently cover drug names, indications, dosages, adverse reactions, approval processes, and compliance requirements. Units include dosage (mg, g), time (hours, days), and percentages (%). Precision and standardization are critical for numerical values.
Constraints Imposed by These Characteristics on "Multi-Turn Conversations and Prompts"
The specialized and rigorous nature of medical affairs documents demands that the dialogue system accurately comprehend complex statements. It must strictly adhere to the original text when providing answers, avoiding improvisation. In multi-turn conversations, users may progressively refine questions, for example, moving from a broad policy area to specific implementation details. This requires the system to maintain conversational coherence and precisely locate relevant paragraphs within a large regulatory framework. Prompt design must guide the model to prioritize information retrieval from the knowledge base. For uncovered or ambiguous questions, the system should prompt users for more information or direct them to human assistance. Furthermore, timely updates to regulations mean the knowledge base must regularly synchronize with the latest content to ensure answer accuracy and prevent outdated or incorrect guidance.
Configuration Settings
| Configuration Item | Suggested Value | Rationale for this Value |
|---|---|---|
maxContext | 8192 | Medical affairs documents are dense. A longer context window is needed to understand complex contexts and related clauses. |
temperature | 0.1 | Strict adherence to regulatory text. A low temperature reduces model improvisation, improving answer accuracy and reliability. |
similarity_threshold | 0.85 | Ensures recalled knowledge chunks are highly relevant to the user's query, preventing the introduction of inaccurate regulatory clauses. |
recall_top_k | 5 | Balances recall efficiency and relevance, ensuring coverage of multiple potentially relevant clauses. |
prompt_template | See example below | Clearly instructs the model to act as a medical affairs expert, strictly answering based on knowledge base content, and guiding multi-turn follow-ups. |
retry_count | 3 | Addresses network fluctuations or occasional model failures, improving dialogue stability. |
Common Pitfalls
- Dialogue flow interruption with an "uncaught exception" message. This typically results from incorrect
modelIdorapiKeyconfigurations, causing communication failure with the large model API. - Model replies that do not cite knowledge base content, instead generating generic answers. This may occur if the
prompt_templatefails to effectively guide the model to perform knowledge retrieval, or ifsimilarity_thresholdis too low, leading to the recall of irrelevant chunks. - In multi-turn conversations, the model fails to understand the context of the user's subsequent follow-up questions. This usually happens when
maxContextis set too small, truncating dialogue history and causing the model to lose previous contextual information.
How to Verify Configuration
- Conduct a series of multi-turn dialogue tests. Verify the model maintains contextual coherence and provides accurate regulatory citations when questions are progressively refined.
- Check if model responses explicitly cite clauses or paragraph numbers from the knowledge base. Confirm the traceability of answers.
- Simulate regulatory updates and then test. Confirm that after knowledge base synchronization, the model answers with the latest regulatory content and does not cite superseded old clauses.
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.