Data Characteristics for This Category
Data sources for respiratory system regulatory submissions primarily include clinical trial reports, pharmacology and toxicology study reports, manufacturing process and quality control documents, and non-clinical study reports. Data update frequency is relatively low, mainly concentrated during new drug development and post-market change applications. Document structures are complex. They typically contain substantial structured data (e.g., laboratory test results, adverse event codes, dosage units) and unstructured text (e.g., clinical study summaries, expert opinions, literature reviews). Fields and units are highly specialized. Examples include lung function indicators (FEV1, FVC, in liters or percentage), arterial blood gas analysis (PaO2, PaCO2, in mmHg), and drug concentrations (ng/mL, μg/L). These require extreme precision and consistency.
Constraints on Multi-Turn Conversations and Prompts Due to These Characteristics
The data characteristics of respiratory system submission documents impose specific constraints on multi-turn conversation and prompt design. First, complex document structures require the dialogue system to effectively handle long texts and cross-document references. For example, discussing clinical endpoints may require consulting multiple clinical trial reports simultaneously. Second, highly specialized fields and units mean prompts must include precise terminology and understand unit conversions to avoid errors from unit confusion. Low data update frequency makes the accuracy and consistency of historical data particularly important. The dialogue system should prioritize authoritative sources and the latest versions during retrieval. Finally, the mix of structured and unstructured data in the documents requires prompt design to guide the model to effectively switch and integrate between them. An example is extracting key numerical values from tabular data and combining them with text descriptions for reasoning.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 12000 characters | Respiratory system submission documents are extensive. This ensures a single request covers a sufficiently long context, reducing information loss. |
Recall Count | top 8 | Ensures comprehensive retrieval results, covering clinical trials, pharmacology, toxicology, and other aspects. |
Similarity Threshold | 0.75 | Ensures retrieved results are highly relevant to the user query, avoiding inaccurate or irrelevant specialized information. |
Segment Length | 800 characters | Balances text semantic integrity with segment processing efficiency, preventing semantic drift from overly long segments. |
Rerank Return Count | top 5 | Refines sorting after initial recall, prioritizing the most relevant information. |
UPLOAD_FILE_MAX_SIZE | 500 MB | Supports uploading large documents such as clinical study reports and non-clinical study reports. |
Three Common Mistakes
- Dialogue model output contains confusing specialized terminology or unit errors. This occurs when prompts fail to sufficiently emphasize the precision of specialized terms and strict unit consistency.
- The model cannot accurately link data points across different documents in multi-turn conversations, such as the relationship between clinical trial results and adverse events. This happens when the system lacks sufficient context management mechanisms or prompts do not guide the model to integrate cross-document information.
- File parsing or knowledge base construction times out after uploading large submission documents. This may be due to
PARSE_FILE_TIMEOUT_SECONDSbeing set too short or insufficient system resources.
How to Verify Configuration
- Conduct multi-turn dialogue tests for key submission questions related to typical respiratory system diseases (e.g., asthma, COPD). Check if the model's understanding and citation of specialized terms, dosage units, and clinical indicators are accurate.
- Upload multiple types of respiratory system submission documents (e.g., clinical reports, pharmacology reports, CMC files). Observe if the knowledge base construction process completes smoothly and verify if core information can be retrieved through dialogue.
- Simulate scenarios where regulatory review experts ask questions. Evaluate if the model can provide coherent and accurate answers regarding drug mechanisms of action, clinical efficacy, and safety data based on the provided documents, and correctly cite source documents.
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.