Data Characteristics
Regulatory affairs data in the biopharmaceutical sector primarily originates from regulations, guidelines, technical review requirements, and approval documents published by national drug regulatory agencies (e.g., NMPA, FDA, EMA). It also includes internal Standard Operating Procedures (SOPs) and project submission documents. This data typically updates quarterly or annually, with ad-hoc updates for major policy changes. Documents are mainly in PDF, Word, and XML formats, containing extensive unstructured text and tables. Key fields include regulation article numbers, document version numbers, effective dates, scope, drug classifications, submission pathways, technical requirement parameters (e.g., content limits, test methods, stability data), and specific units (e.g., mg, mL, %).
Constraints Imposed by Data Characteristics on Multi-turn Conversations and Prompts
The low update frequency of regulatory affairs data means knowledge base maintenance costs are relatively manageable after initial construction, but timely updates for regulatory revisions are crucial. Diverse document formats, especially complex tables and images within PDFs and Word documents, demand high-quality text extraction and knowledge chunking to prevent information loss or context disruption. Multi-turn conversations require precise citation of regulatory articles and technical parameters, necessitating high recall accuracy and original text referencing capabilities. The rigor of fields and units determines answer accuracy; for instance, if a user asks about "dissolution limits for a certain preparation," the system must differentiate preparation types and provide specific values and units. Prompt design must emphasize extracting and integrating key information to handle cross-references and multi-conditional judgments common in regulatory Q&A.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Size) | 500–800 characters | Ensures the completeness of regulatory articles, preventing truncation of key information while balancing recall efficiency. |
Recall count (Number of Retrieved Chunks) | Top 5–8 chunks | Regulatory Q&A often involves multiple regulations or conditions; increasing retrieval count helps ensure comprehensive coverage. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Guarantees high relevance between retrieved results and the query, reducing interference from irrelevant information and improving Q&A precision. |
Rerank result count (Number of Reranked Chunks) | Top 3–5 chunks | Reranks retrieved results to prioritize regulatory articles or SOP steps that best match user intent. |
maxContext | 3000–4000 tokens | Supports multi-turn conversation context, especially for regulatory interpretation and complex process inquiries, ensuring conversational coherence. |
temperature | 0.1–0.3 | Reduces the randomness of model responses, ensuring the rigor and accuracy of regulatory Q&A and preventing the generation of fabricated information. |
Common Pitfalls
- Issue: During a conversation, the model incorrectly or incompletely cites specific regulatory articles. Reason: Inadequate knowledge chunking strategy, leading to a single regulatory article being split across different knowledge blocks, or critical information being lost during chunking.
- Issue: A user asks about the steps of a submission process, and the model's answer is vague or lacks specific guidance. Reason: The prompt fails to effectively guide the model to extract procedural or step-by-step information from the knowledge base, or the knowledge base does not structure SOPs appropriately.
- Issue: The model cannot reiterate the content of an uploaded Excel file containing technical requirements during a conversation. Reason: The knowledge base might not correctly parse table structures when processing
xlsxfiles, leading to data not being effectively indexed or converted into retrievable text.
Validation Steps
- Simulate user queries to verify if the model can accurately cite specific articles from the "Drug Registration Management Measures," including article numbers and content.
- Test if the model can provide detailed process steps and required document lists for a specific stage of new drug registration (e.g., clinical trial application, marketing authorization application), confirming information completeness.
- Check if the model can provide correct values and units for questions involving specific technical parameters (e.g., impurity limits, stability requirements) and trace them back to original documents.
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.