Multi-Turn Conversations and Prompts for Pharmacovigilance

Data Characteristics in this Category

Medical affairs departments collect pharmacovigilance data from clinical study reports, post-market surveillance, adverse event reporting systems (e.g., MedDRA, WHO-ART), medical literature, and regulatory databases. This data updates frequently. Clinical trial data typically updates at the end of each study phase. Post-market surveillance data streams in continuously and in real time. Document structures vary, including unstructured free text (e.g., patient descriptions, physician notes), semi-structured tabular data (e.g., adverse event report forms), and structured coded data (e.g., drug ATC codes, disease ICD-10 codes). Fields and units are highly specialized and standardized. Examples include adverse event frequency, severity grading, drug dosage units (mg/kg, U/ml), time units (hours, days, weeks), and precise medical terminology.

Constraints on Multi-Turn Conversations and Prompts

The specialized and diverse nature of pharmacovigilance data imposes specific constraints on multi-turn conversation and prompt construction. Free-text patient descriptions require strong semantic understanding to accurately identify and structure key information. High-frequency, real-time data updates demand rapid knowledge base synchronization to ensure AI response timeliness and accuracy. Diverse document structures mean prompt design must consider different data source characteristics. For example, prompts might extract specific fields from structured data or summarize unstructured text. The strictness of fields and units requires prompts to precisely guide the AI to extract and present values with correct units, avoiding confusion. For instance, prompts must differentiate drug dosage from patient weight. Furthermore, standardized medical terminology requires prompts to recognize and map synonyms and near-synonyms, ensuring precise answers.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`8`	Pharmacovigilance issues often require tracing multiple historical turns for a complete judgment.
`Chunk size` (Chunk Size)	`400–600 characters`	Medical literature and reports have long paragraphs. Shorter chunks risk cutting key information; longer chunks increase recall noise.
`Similarity threshold` (Similarity Threshold)	`0.78`	Ensures recalled medical text is highly relevant to the query intent, reducing misjudgment risk.
`Recall count` (Recall Count)	`Top 6 entries`	Balances recall efficiency and information completeness, covering potentially relevant information.
`Rerank result count` (Rerank Return Count)	`Top 3 entries`	Reduces the number of tokens processed by the large model, focusing on the most core recall results.
`temperature`	`0.3`	Pharmacovigilance requires accurate and objective responses. Low temperature reduces content randomness.

Three Common Mistakes

Medical terminology confusion or unit errors in conversation results: This occurs when prompts do not sufficiently emphasize the precision requirements of medical terminology, or when the knowledge base lacks adequate synonym mapping.
AI fails to remember key information from previous turns in multi-turn conversations: This happens when the maxContext parameter is set too low, truncating historical conversations and affecting context understanding.
AI responses are outdated and do not reflect the latest adverse event reports: This indicates an untimely knowledge base update mechanism or an improperly configured refreshInterval parameter, failing to synchronize the latest data promptly.

How to Confirm Proper Configuration

Conduct a series of multi-turn conversation tests covering common adverse event types, drug dosage inquiries, and patient characteristic descriptions. Check the accuracy and consistency of AI responses.
Randomly select the latest adverse event reports. Simulate user questions to verify if the AI can cite the latest data and provide correct explanations. Compare AI responses with actual reports.
Check the accuracy of medical terminology and the correctness of drug dosage and time units in AI responses. Ensure compliance with standard medical norms.
Observe token consumption and response times under various query complexities. Evaluate if system performance meets practical needs.

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.