Data Characteristics for This Category
Contract Sales Organizations (CSOs) prepare registration and declaration documents using data primarily from pharmaceutical companies. This includes raw R&D data, clinical trial reports, non-clinical study reports, manufacturing process documents, quality standards, stability study data, and previous submission documents. Data exists in both structured (e.g., clinical trial databases, ICH M4Q/M4R format CTD modules) and unstructured forms (e.g., research report PDFs, internal email communications, expert opinion Word documents). Core R&D and clinical data update dynamically as projects progress. Regulatory requirements and guidelines revise periodically. Document structures are complex, containing specialized terminology, abbreviations, and specific formatting requirements. Fields and units include dosage, concentration (e.g., mg/mL, μg/kg), time points (e.g., hours, days, weeks), statistical indicators (e.g., P-values, confidence intervals), and biological indicators (e.g., PK/PD parameters). These must strictly follow ICH guidelines and national drug regulatory agency requirements.
Constraints Imposed by These Characteristics on Multi-turn Conversation and Prompts
The complex data characteristics of CSO registration and declaration documents impose multiple constraints on multi-turn conversation and prompt design. First, the vast amount of specialized terminology and abbreviations requires the model to have strong semantic understanding. Prompts must clearly guide the model to identify and interpret domain-specific vocabulary. Second, diverse data sources make precise information retrieval challenging during conversations. Prompts need to guide the model to extract and integrate key information from different document types. For example, when querying "adverse event incidence of a certain drug in clinical trials," the model must retrieve both clinical study reports and safety databases. Third, dynamic data updates require the model to identify the latest version information. Prompts should include context information like timestamps or version numbers. Finally, strict regulatory compliance means conversation results must be accurate. Prompt design must emphasize fact-checking and source citation to prevent the model from generating hallucinations or misleading information, especially for critical fields like dosage, indications, and contraindications.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for This Value |
|---|---|---|
maxContext | 8000–16000 tokens | Accommodates complex medical terminology, multi-document context, and regulatory clauses, preventing information truncation that impacts understanding. |
temperature | 0.1–0.3 | Ensures accuracy and consistency of generated content, reduces hallucination risk, and meets regulatory rigor requirements. |
Recall count | 10–20 entries | Covers a broader range of relevant document snippets, especially when querying cross-document information, improving recall rate. |
Similarity threshold | 0.75–0.85 | Filters out irrelevant or weakly related text, ensuring precision of recalled content and reducing noise interference. |
Rerank result count | 5–8 entries | Performs secondary filtering and reordering of recall results, prioritizing the most relevant and authoritative information snippets. |
Workflow Max Run Count | 100 times | Addresses complex queries that may involve multiple knowledge base retrievals, external tool calls, and logical judgments. |
Three Common Mistakes
- Missing critical data or regulatory clauses in conversations: This may be due to insufficient knowledge base recall or prompts that do not clearly guide the model to perform comprehensive retrieval.
- AI response variables overlaying results from previous variables: This often results from incorrect variable passing logic between nodes in the workflow, or the model failing to correctly distinguish context when generating responses.
- The interface not displaying expected questions after enabling input guidance: This is typically due to improper configuration of the guidance vocabulary or insufficient relevance to the knowledge base content, preventing the system from matching appropriate guidance questions.
How to Confirm Proper Configuration
- Simulate key submission scenarios by inputting complex questions containing specialized terminology and regulatory requirements. Check if the model's responses are accurate, complete, and cite correct source documents.
- For core information such as drug dosage and adverse events, conduct multi-turn follow-up questions. Verify if the information provided by the model remains consistent and correct across different turns.
- Test workflow stability under high concurrent calls. Observe for
nonevalues or other anomalies, and check logs for timeout or error messages.
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.