Multi-turn Conversation and Prompt Design for Structured

Data Characteristics in Medical Affairs

Medical affairs data originates from clinical study reports (CSRs), pharmacovigilance reports (PSURs), medical literature, regulatory submission documents, and internal medical communication materials. These documents are typically in PDF or Word format. They contain specialized terminology, abbreviations, charts, and tables. Updates are driven by drug development cycles and regulatory requirements. For example, PSURs update semi-annually or annually, while clinical study data generates continuously as trials progress. Document structures are highly standardized, adhering to international guidelines like ICH GCP and GVP. They feature clear section divisions such as study background, methods, results, and discussion. Fields and units include dosage (mg/kg), frequency (QD/BID), efficacy endpoints (e.g., ORR, PFS), adverse event (AE/SAE) codes (MedDRA), statistical p-values, and confidence intervals. Precision and consistency requirements are very high.

Constraints on Multi-turn Conversations and Prompts

The highly structured and specialized nature of medical affairs documents requires multi-turn conversation systems to accurately understand and extract specific information. Specialized terminology and abbreviations challenge the LLM's domain knowledge and semantic understanding. Prompt design must incorporate extensive domain context. Varying document update frequencies necessitate sophisticated version management and incremental update mechanisms for the knowledge base. This ensures conversations rely on the latest data. In multi-turn conversations, users may ask follow-up questions about statistical significance or adverse event associations. This requires the system to integrate information from multiple documents for inference. High demands for data precision and consistency mean conversation results need strict traceability. Prompts should guide the model to cite source documents and specific sections in its responses.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`8192`	Ensures capacity for complex medical concepts and multi-turn conversation context.
`Chunk size` (Chunk Length)	`800–1200 characters`	Balances semantic completeness and chunk retrieval efficiency, avoiding truncation of critical information.
`Recall count` (Retrieval Count)	`Top 10–15`	Covers enough potentially relevant document chunks to address multi-faceted queries.
`Similarity threshold` (Similarity Threshold)	`0.78–0.85`	Balances recall and precision, filtering out irrelevant professional literature.
`Rerank result count` (Reranked Return Count)	`Top 5`	Focuses on the most relevant results, improving conversation response speed and accuracy.
`temperature`	`0.2–0.4`	Reduces randomness in model output, ensuring the rigor of medical information.

Common Pitfalls

The model fails to correctly parse numerous professional terms or abbreviations in the conversation. This leads to off-topic answers or hallucinations. This occurs when prompts do not adequately provide domain glossaries or context, or when the knowledge base lacks effective definitions and associations for these terms.
When a user asks for a data source, the model provides only a generalized answer instead of a specific document path or section. This usually indicates the vector database lacks metadata storage or retrieval capabilities, preventing association with original document information during retrieval.
In multi-turn conversations, the model fails to retain memory of specific metrics or patient populations from previous turns. It asks repetitive questions or gives inconsistent answers. This indicates the maxContext parameter is set too low, unable to carry the full conversation history, leading to context loss.

Verification of Configuration

For a set of medical questions containing specialized terms and abbreviations, verify if the model can accurately understand and provide consistent answers in multi-turn conversations. Cross-reference the data mentioned in the answers with the original documents.
Randomly select key information points from conversations. Check if the model's answers provide accurate document sources (e.g., document name, version number, section title) to verify traceability.
In simulated complex follow-up scenarios, observe if the model maintains conversation context, avoids repetitive questioning, or omits previously clarified information. Determine the appropriate maxContext threshold by comparing performance across different settings.

The values provided are common starting points. Measure them against your 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.