Data Characteristics in This Category
Real-World Study (RWS) product data primarily originates from Electronic Health Records (EHR), medical insurance claims databases, disease registries, wearable device data, and patient-reported outcomes (PRO). This data is typically heterogeneous, comprising both structured (e.g., diagnosis codes ICD-10, drug codes NDC, lab test results) and unstructured (e.g., physician notes, imaging report text) types. Data update frequencies vary from daily (EHR) to quarterly/annually (medical insurance claims), leading to timeliness differences. Document structures are complex; for example, clinical trial reports can span hundreds of pages with multiple chapters and appendices. Fields and units are diverse; drug dosages might be expressed in milligrams (mg), units (U), or milliliters (mL), and time fields involve various formats, often accompanied by missing values or inconsistent encodings.
Constraints Imposed by These Characteristics on "Multi-Turn Conversations and Prompts"
The heterogeneity and complex structure of real-world study data challenge the coherence of multi-turn conversations. Unstructured text content requires efficient semantic understanding and entity extraction to ensure accurate referencing in subsequent turns. Differences in timestamps and update frequencies across multiple data sources require the conversation system to recognize data timeliness when citing information. For example, tracing a patient's medical history may require integrating multiple reports from different time points. The variety of field units necessitates prompt design that considers unit conversion and standardization to avoid misinterpretation due to unit confusion. Additionally, common missing values and inconsistent encodings in the data require the system to have a degree of fault tolerance and reasoning capability, guiding users to clarify or supplement information during conversations to maintain effectiveness.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 30 | Retains sufficient historical turns to understand contextual relationships in complex disease progression and medication history. |
Chunk size (Segment Length) | 800-1200 characters (characters) | Accommodates the long paragraphs and high information density typical of real-world study reports, ensuring the completeness of information recalled in a single instance. |
Recall count (Number of Recalled Items) | Top 8 entries (top 8) | Considering the numerous and overlapping RWS data sources, increasing the number of recalled items helps cover more comprehensive relevant information. |
Similarity threshold (Similarity Threshold) | 0.75 | Balances accuracy and recall, filtering document segments highly relevant to biomedical concepts. |
Rerank result count (Number of Reranked Items) | Top 5 entries (top 5) | In multi-turn conversations, this streamlines and focuses on the most important information, reducing cognitive load and improving response efficiency. |
prompt_template | Customize specific biomedical domain terminology | Ensures the prompt accurately guides the model to understand professional terminology, drug names, and disease characteristics. |
Three Common Mistakes
- Insufficient context turns displayed in conversation details, leading to the model's inability to link to previous discussions. This is caused by a
maxContextparameter set too low or an improperly configured context storage mechanism. - The AI conversation enters an infinite loop or cannot exit when generating text repeatedly. This manifests as generating similar content repeatedly. This is due to the
question classificationnode's judgment logic being insufficiently rigorous, failing to accurately identify termination conditions or effectively utilize the conclusion of the previous question. - When processing drug dosages or test results, the model provides incorrect values or units. This occurs because the prompt does not explicitly require unit standardization, or the knowledge base does not uniformly handle heterogeneous units.
How to Verify Configuration
- Test various complex real-world study queries to check if the system accurately identifies and references key entities in multi-turn conversations, such as patient ID, diagnosis time, and drug dosage.
- Verify that in multi-turn conversations, the system can correctly process and integrate information from different data sources (e.g., EHR and medical insurance data), and can identify information timestamps and timeliness.
- Simulate queries containing missing values or inconsistent encodings to observe if the system can guide users to clarify information or make reasonable inferences when necessary, ensuring a smooth conversation flow.
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.