Data Characteristics in This Category
Pharmacovigilance data primarily originates from clinical trial reports, real-world evidence (RWE) data, adverse drug reaction (ADR) reports, and drug labels. This data updates frequently, especially during the rapid monitoring period after a new drug's market launch. Document structures typically include both structured and unstructured sections. For example, an adverse event report might contain fields such as patient demographics, medication history, adverse reaction descriptions, and diagnostic results. Clinical trial protocols and reports have more complex structures, covering study objectives, inclusion/exclusion criteria, investigational drug information, dosage, treatment regimens, and safety assessment indicators. Field types are diverse, including text descriptions, numerical values (e.g., dose units like mg, µg; frequency units like times/day, times/week), date/time (report date, occurrence date), boolean values (whether a serious adverse event), and categorical labels (adverse reaction type, severity).
Constraints Imposed by These Characteristics on Multi-turn Conversations and Prompts
The high update frequency of pharmacovigilance data requires the knowledge base to have an efficient indexing update mechanism to ensure retrieved information is always current. Diverse document structures and field types demand flexible prompt design, capable of handling both structured queries and understanding medical terminology and context within unstructured text. For instance, when querying a specific adverse event, prompts must guide the model to accurately identify key information such as the adverse reaction name, drug name, time of occurrence, and patient characteristics. The presence of numerical fields and units requires the model to perform dimension matching and numerical comparisons in multi-turn conversations, preventing misjudgments due to inconsistent units. The specialized and complex nature of medical terminology means prompts must include sufficient domain knowledge to reduce ambiguity and improve retrieval accuracy. Focus on safety assessment indicators also requires prompts to guide the model to concentrate on key data points related to risk assessment.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 4096 | Pharmacovigilance reports are lengthy; sufficient context is needed for semantic understanding. |
Recall Count | 5–8 entries | Ensures coverage of relevant adverse events and drug information, preventing omissions. |
Similarity Threshold | 0.75 | Clinical terminology is strict; a high threshold ensures retrieval precision. |
Chunk Length | 500–800 characters | Balances paragraph completeness and retrieval efficiency, retaining key information. |
Reranked Top K | Top 3 entries | Focuses on the most relevant evidence, reducing model processing burden. |
MaxTokens (LLM) | 1024 | Allows the model to generate detailed adverse event analyses and recommendations. |
Three Common Pitfalls
- The model's response does not cite any knowledge base content: This occurs because the retrieved relevance score is too low, failing to meet the model's internal citation threshold, or because the prompt does not effectively guide the model to utilize retrieval results.
- The model confuses different patient or drug information in multi-turn conversations: This happens when the prompt does not explicitly instruct the model to differentiate between different entities, or the context window is insufficient to hold all critical information across multiple turns.
- Retrieval results include non-target chunk IDs: This is due to an overly coarse knowledge base chunking strategy, or the retrieval algorithm failing to accurately identify the most relevant chunk boundaries for the query intent.
How to Verify Correct Configuration
- For typical adverse drug event queries, verify whether the model's response accurately cites key information from clinical trial reports or ADR databases, such as drug names, adverse reaction types, and frequency of occurrence.
- Use a series of complex multi-turn conversation tests to confirm that the model maintains contextual consistency and provides logically coherent answers when asked about adverse reactions at different time points, different dosages, or in different patient populations.
- Execute queries containing medical terminology and numerical units. Check if the model can correctly understand and process information like dosage (e.g.,
10 mg/kg) and frequency (e.g.,Daily Twice), and return corresponding report chunks.
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.