Data Characteristics in This Domain
Regulatory submission documents in the biopharmaceutical field cover registration applications for drugs, medical devices, and similar products. Data sources are extensive. They include clinical trial data (e.g., CRFs, SAE reports), pharmaceutical research data (e.g., manufacturing processes, quality standards, stability studies), non-clinical study reports, regulatory documents (e.g., ICH guidelines, NMPA guidelines), and previous submission cases. These documents have a relatively low update frequency, primarily updating after regulatory revisions, supplementary research data, or product approval. Document structures are highly standardized, often following specific submission formats like the CTD format. Fields and units are extremely rigorous; for example, dose units are mg/kg, concentration units are ng/mL, and they often contain complex medical terminology, abbreviations, and statistical indicators.
Constraints Imposed by These Characteristics on "Multi-turn Conversation and Prompts"
The high standardization and rigor of regulatory submission documents require multi-turn dialogue systems to precisely identify medical terminology and regulatory requirements when understanding user intent. Low update frequency means the knowledge base content is relatively stable, but retrieval accuracy and timeliness are critical to avoid citing outdated or superseded regulations. Complex document structures demand deep semantic understanding from the system to extract key information from lengthy documents and effectively link content across different sections. Rigorous field and unit requirements necessitate prompt design that guides users to clarify query scope and strictly validates returned results to ensure correct units and values, preventing serious consequences from misinterpretation.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for This Value |
|---|---|---|
maxContext | 8–12 turns | Regulatory submission queries often require a longer context to track complex logic and regulatory provisions. |
Chunk size (Segment Length) | 800–1200 characters | Regulatory provisions and research report paragraphs are long, ensuring semantic completeness. |
Recall count (Recall Count) | Top 5–8 items | Ensures coverage of relevant regulations, research reports, and cases, improving recall rate. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements, above 0.75 | Ensures high relevance of retrieval results, avoiding the introduction of irrelevant information. |
Rerank result count (Reranked Return Count) | 3–5 items | Selects the most relevant and authoritative document snippets based on a high recall rate. |
temperature | 0.2–0.4 | Reduces the randomness of model-generated content, ensuring the rigor and accuracy of responses. |
Three Common Pitfalls
- Dialogue responses are too short to fully explain regulatory provisions or research conclusions. This occurs because the
max_tokensparameter is set too low, limiting the model's output length. - The system produces data errors or unit confusion when handling user questions about specific drug dosages or adverse reactions. This happens because prompts fail to effectively guide the model to focus on strict validation of numerical fields and their units.
- After enabling input guidance, system errors occasionally occur during conversations, preventing normal interaction. This is due to incorrect custom vocabulary address configuration or content format not meeting system requirements, leading to parsing failure.
How to Confirm Proper Configuration
- For questions about complex regulatory provisions, check if multi-turn conversations can continuously track context and provide coherent and accurate explanations.
- Verify the accuracy and consistency of system output when answering questions involving specific values, units (e.g., mg/kg, ng/mL), and medical abbreviations.
- Test different types of regulatory submission queries (e.g., clinical data, pharmaceutical research, regulatory interpretations) to evaluate the system's ability to effectively extract and integrate information from different document structures, and assess whether the output content complies with regulatory requirements.
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.