Data Characteristics
Data for intelligent triage in pharmaceutical regulatory document preparation primarily originates from drug labels, clinical trial reports, regulatory guidelines, approval opinions, and medical literature. This data updates infrequently, typically with drug approvals, indication expansions, or regulatory revisions. Document structures are predominantly unstructured text, containing extensive specialized terminology, abbreviations, and complex tables. Fields include drug names, indications, dosage and administration, adverse reactions, pharmacology and toxicology, and clinical data. Units involve dosage (e.g., mg, g), time (e.g., h, d), and concentration (e.g., μg/mL). Measurement units may vary across different sources.
Constraints on Multi-Turn Conversations and Prompts
Intelligent triage data characteristics impose specific constraints on multi-turn conversation and prompt design. First, unstructured text and specialized terminology require robust semantic understanding from the dialogue system to accurately identify medical entities and relationships. Second, infrequent data updates mean historical dialogue context has less impact on results, but knowledge base accuracy and consistency are critical. Complex tabular data requires the Retrieval Augmented Generation (RAG) mechanism to effectively parse table content and integrate it into dialogue responses. Discrepancies in measurement units necessitate the dialogue system to perform unit conversions or explicitly state original units in responses to avoid misunderstandings. Furthermore, the rigor of regulatory documents demands high factual accuracy in multi-turn conversations. Prompts must guide the model to prioritize citing original evidence and limit creative generation.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8 turns | Regulatory document Q&A often requires maintaining a longer contextual association to avoid missing critical information. |
temperature | 0.1 | Ensures rigor and accuracy in responses, reducing the model's propensity for creative generation. |
top_p | 0.3 | Further limits the diversity of model generation, focusing on factual information within the knowledge base. |
Recall count (Recall Count) | 10 items | Ensures sufficient relevant document snippets are recalled from vast regulatory documents, improving coverage. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements in the 0.75-0.85 range | Balances recall accuracy and comprehensiveness, avoiding interference from irrelevant content. |
prompt | Include "Please answer the question in detail based on the provided regulatory documents, and cite the original source." | Clearly instructs the model to strictly answer based on the documents and requires providing traceability information. |
Common Pitfalls
- The large language model occasionally returns empty responses. This may be due to upstream model API call timeouts or internal model processing exceptions.
- AI dialogue output contains garbled citation symbols. This typically occurs because the model generates Markdown-formatted citation markers, but front-end rendering or log parsing fails to process them correctly.
- Setting
CHAT_FILE_EXPIRE_TIMEto a small value causes user-uploaded regulatory document files to expire prematurely, affecting dialogue continuity.
Verification Steps
- Ask multi-turn questions about a specific drug. Check if responses accurately cite data from the drug label or clinical trial reports and verify the cited original source locations.
- Simulate questions about drug dosage and administration or adverse reactions. Verify if unit consistency (e.g., dosage units, time units) is maintained or explicitly marked in responses, and compare with original data.
- Test complex queries involving multiple fields (e.g., drug name, indication, dosage and administration). Observe if the model can accurately integrate information and provide coherent answers.
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.