Data Characteristics in this Category
Pharmacovigilance (PV) regulatory data primarily originates from regulations, guidelines, Standard Operating Procedures (SOPs), and internal policy documents. These documents are typically stored as PDFs, Word files, or in internal knowledge bases. The content is highly structured, containing numerous definitions, flowcharts, responsibility assignments, and reporting templates. Data update frequency is relatively low, occurring mainly when regulations are revised, new drugs are launched, or internal processes are optimized. Documents often include medical terminology, drug names, adverse event classification codes (e.g., MedDRA), dosage units (mg, μg), and time units (days, hours). Accuracy and consistency requirements are extremely high.
Constraints Imposed by these Characteristics on "Multi-Turn Conversations and Prompts"
The structured nature of pharmacovigilance documents requires the knowledge base to have precise paragraph segmentation and semantic understanding capabilities. This prevents fragmented information from cross-chapter references. The low update frequency means model training and knowledge base synchronization do not need to be overly frequent, but each update requires strict quality verification. The presence of specialized terminology and measurement units in documents challenges the model's ability to maintain accuracy and consistency in context during multi-turn conversations. For example, when a user asks about "reporting periods," the model must differentiate reporting deadlines for various adverse event types and accurately understand the definition of "day" (e.g., calendar day vs. business day). Prompt design must guide the model to focus on these details and handle abbreviations or non-standard expressions in user input.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Size) | 300–500 characters | Accommodates the concise nature of regulatory provisions, ensuring semantic completeness. |
Overlap Size | 50 characters | Ensures contextual continuity between paragraphs, handling process descriptions that span multiple sections. |
Recall count (Recall Count) | Top 5–8 entries | Pharmacovigilance documents are highly interconnected; increasing recall helps achieve comprehensive understanding. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Ensures the precision of recalled content, avoiding interference from irrelevant regulations. |
maxContext | 4096 tokens | Accommodates the accumulation of specialized terminology and process details in multi-turn conversations. |
Conversation History Retention | 7 days | Meets the continuity requirements for general query sessions while balancing storage costs. |
Three Common Mistakes
- Conversation history retention is too short. This leads to loss of contextual information when users ask complex multi-turn questions, preventing effective continued interaction. This results from improper
Conversation History Retentionsettings or not considering the continuity needs of user queries. - Knowledge base permission configuration is incorrect. This allows users to query internal policies or sensitive information they should not access, leading to information leakage risks. This stems from
Knowledge Base Access Controlnot being precisely linked to actual business roles. - Prompts do not explicitly instruct the model to pay attention to measurement units and time definitions. This leads to vague or incorrect answers when dealing with reporting deadlines or dosage calculations. This is due to insufficient consideration of the unique precision requirements of the pharmacovigilance domain during prompt design.
How to Confirm Proper Configuration
- Simulate multi-turn conversations. Ask questions involving different reporting periods and event types. Verify if the model's understanding of time units (days, hours) is consistent and accurate.
- Attempt to query internal SOPs containing sensitive information. Confirm if the model returns "No access permission" or does not respond based on configured permissions.
- For queries about complex processes, check if the model maintains contextual coherence during the conversation and accurately references key terms or data points mentioned earlier in the dialogue.
Note: 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.