Data Characteristics for This Category
Phase II-III clinical R&D documents primarily include Clinical Study Protocols, Investigator's Brochures (IB), Case Report Forms (CRF), Statistical Analysis Plans (SAP), ethical approvals, and informed consent forms. These documents typically exist as PDFs, Word files, or scanned images. They are lengthy, complex in structure, and contain extensive specialized terminology, medical abbreviations, charts, and data. Document update frequency is relatively low, but updates often involve critical content revisions. Fields and units require high standardization, such as dosage units (mg, μg, mL), time units (days, weeks, months), and laboratory indicators (mmol/L, U/L), often accompanied by specific ranges or normal values.
Constraints Imposed by These Characteristics on "Multi-turn Conversations and Prompts"
The complexity and specialized nature of these documents require multi-turn dialogue systems to possess strong semantic understanding and contextual correlation capabilities to accurately capture user intent. Lengthy documents mean a single retrieval is insufficient to answer complex questions, necessitating the system to progressively focus information across multiple turns. The presence of specialized terminology and abbreviations requires prompt design to consider term mapping or provide sufficient context. Content in charts and scanned images is difficult to parse directly, limiting the effectiveness of pure text retrieval. This necessitates OCR and chart structuralization during the preprocessing stage. Low update frequency implies relatively stable content after knowledge base construction, but critical revisions must be synchronized promptly. The standardization of fields and units requires prompts to clearly define numerical types and ranges when asking questions, avoiding ambiguous answers.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk Size | 800–1200 characters | Balances semantic completeness with retrieval efficiency, preventing overly long paragraphs from diluting key information. |
Recall Count | Top 5 | Given the complexity of clinical documents, an appropriate increase in recall quantity covers more potentially relevant information. |
Similarity Threshold | 0.75 | Ensures retrieved content is highly relevant to the query, filtering out noise and improving answer accuracy. |
Rerank Count | 3 | Further refines the most relevant snippets through a secondary sort on top of the initial recall, enhancing user experience. |
Max History Messages | 8 turns | Maintains a sufficiently long conversational context to support in-depth inquiries into clinical protocol details. |
temperature | 0.3 | Reduces the randomness of model-generated content, ensuring the rigor and objectivity of answers, and preventing fabrication. |
Three Common Mistakes
- Model returns "connection error" or remains unresponsive for an extended period. Common causes include improper proxy configuration or model service interface timeouts, especially when using externally deployed models.
- Inability to accurately understand medical abbreviations or specific terminology during a conversation, leading to irrelevant answers. This is due to a lack of corresponding abbreviation dictionaries or contextual explanations in the knowledge base.
- When a user asks about content in charts or scanned images within a document, the system returns empty or cannot answer. This occurs because non-textual information was not effectively identified and structured during the preprocessing stage.
How to Confirm Correct Configuration
- Conduct multi-turn questioning tests on core clinical study protocols to verify the model's accurate understanding of key indicators, inclusion/exclusion criteria, and dosage regimens.
- Use sentences containing medical abbreviations from the document for questioning to assess if the model can correctly parse and provide relevant information, evaluating its grasp of specialized terminology.
- Simulate user queries for numerical data in documents (e.g., drug dosage ranges, normal laboratory values) to check if the numerical values and units returned by the model are consistent and precise.
Note: The values provided are common starting points and should be measured 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.