Multiturn Conversation and Prompts for Medical Affairs

Data Characteristics

Medical affairs data originates from clinical trial reports, pharmacovigilance data, drug monographs, medical guidelines, academic literature, and regulatory documents. Data update frequencies vary. Clinical trial data is released periodically as research progresses. Pharmacovigilance data involves continuous real-time monitoring and reporting. Drug monographs and medical guidelines are revised based on drug life cycles and medical advancements. Document structures are primarily semi-structured or unstructured text, such as PDF clinical study reports, Word expert consensuses, and adverse event records in databases. Fields and units are highly specialized, including drug dosage (e.g., mg/kg), treatment duration (e.g., days, weeks), adverse event incidence (e.g., percentage), and statistical indicators (e.g., P value, confidence interval).

Constraints for Multiturn Conversation and Prompts

The specialized nature and varied update frequency of medical affairs data impose specific requirements on multiturn conversation context management and prompt design. Clinical trial reports and academic literature are often lengthy and contain extensive specialized terminology and statistical data. Multiturn conversations require a longer context window to maintain coherence and accuracy, preventing misunderstandings due to information loss. For example, after inquiring about a drug's specific indication, a user might ask about clinical efficacy data or adverse reactions for that indication. The system must remember and associate key information, such as drug names and indications, from previous turns.

Differences in data updates also affect prompt configuration. For frequently updated pharmacovigilance data, prompts must guide the model to prioritize the latest information and include timestamps for sources. For complex documents, prompts must emphasize the ability to extract content from specific sections or tables. This ensures the model precisely locates required data points, such as extracting the primary endpoint event rate from a clinical trial report. Accuracy of specialized terminology and units is critical. Prompts must explicitly require the model to retain original professional vocabulary and units in responses, avoiding unnecessary interpretation or conversion that could compromise the rigor of medical information.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`4096 tokens`	Medical literature is complex, requiring a longer context window for coherence.
`prompt`	See description below	Guides the model to accurately understand medical query intent and specialized terminology.
`temperature`	`0.1`	Ensures objective and accurate responses, reducing the risk of hallucination.
`top_p`	`0.8`	Introduces slight diversity for complex queries while maintaining accuracy.
`recall_count`	`8`	Increases retrieval breadth, covering more relevant medical literature segments.
`similarity_threshold`	`0.75`	Ensures professional relevance of recalled content, filtering out irrelevant information.

Recommended prompt value: For medical affairs, the prompt should include clear instructions, such as: "As a medical expert, based on the provided materials, rigorously and accurately answer the user's questions regarding drug [Drug Name] concerning [Indication] in terms of [Clinical Efficacy/Adverse Reactions/Mechanism of Action]. Responses must cite key data and specialized terminology from the original materials and indicate the chapter or page number of the data source. Avoid subjective judgments and vague statements. If information is insufficient, clearly state it."

Common Pitfalls

Responses containing non-medical terminology or incorrect unit conversions often result from prompts not explicitly requiring retention of original professional language, or temperature values set too high, leading the model to generate freely.
In multiturn conversations, the model forgetting drugs or indications mentioned in previous turns, leading to off-topic responses, occurs when maxContext is insufficient to cover the complete conversation history, or the context management mechanism fails to effectively transfer key information.
The system's inability to extract specific data points, such as the incidence rate of a side effect, from lengthy clinical trial reports, is due to prompts not explicitly instructing the model to extract data, or the document parsing stage not effectively identifying and structuring tabular data within reports.

Verification

Select 5–10 typical medical affairs consultation scenarios. Test whether the model can consistently track the conversation topic and accurately answer subsequent questions in multiturn conversations. Pay particular attention to whether it can cite key entities from previous turns.
For queries involving specialized terminology and units, verify that all medical terms and numerical values in the model's response are identical to the original materials. Specifically check the accuracy of units like μg/mL or nM.
Choose several complex clinical trial reports or drug monographs. Ask for data from specific sections or tables within them. Verify that the model can precisely extract and present the data, comparing it with the original document to confirm the accuracy of data points like P value or OR value.

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.