Multi-Turn Conversations and Prompts for II-III Clinical

Data Characteristics in This Domain

II-III clinical trial data originates from various sources. These include Clinical Study Reports (CSR), Investigator's Brochures (IB), protocol amendment documents, ethics committee approval letters, informed consent forms, Case Report Form (CRF) data, Statistical Analysis Plans (SAP), and various supporting research reports. These documents typically exist in PDF, Word, or Excel formats. Data updates frequently, especially during ongoing trials, with frequent generation of protocol amendments, safety reports, and interim analysis results. Document structures are complex, containing extensive specialized terminology, abbreviations, and standardized expressions. Examples include ICD-10 codes for medical terms, mg/kg units for drug dosages, and statistical P-values. Data fields exhibit strong interdependencies and demand extremely high accuracy and consistency.

Constraints Imposed by These Characteristics on Multi-Turn Conversations and Prompts

The complexity of II-III clinical data imposes strict requirements on multi-turn conversation context management. Conversations frequently reference trial design details, drug dosages, or subject inclusion criteria from earlier discussions. The system must accurately recall and comprehend this context. Specialized terminology and abbreviations necessitate strong semantic understanding capabilities in prompts, mapping non-standard user inputs to standard terms. Frequent document updates mean the knowledge base requires efficient synchronization mechanisms to ensure conversations rely on the latest data. Furthermore, strong field interdependencies require prompt design to integrate information across documents. An example is extracting efficacy data from a CSR and linking it to statistical methods in an SAP. Conversations must strictly adhere to medical ethics and regulatory requirements. Prompt design must avoid potentially misleading or inaccurate information.

Configuration Settings

Configuration Item	Recommended Value	Rationale for Recommendation
`maxContext`	`8`	Ensures coverage of at least two key question-answer turns and their related context in multi-turn conversations, maintaining coherence.
`Chunk size` (Chunk Size)	`800–1200 characters` (characters)	Balances semantic completeness of individual text blocks with retrieval efficiency, preventing dilution of key information by overly long texts.
`Recall count` (Recall Count)	`Top 10 entries` (top 10)	Given the specialized nature and information density of II-III clinical documents, increasing the recall count improves relevant retrieval rates.
`Similarity threshold` (Similarity Threshold)	`0.78`	Experimentally validated, this threshold ensures retrieval quality while effectively filtering irrelevant document segments.
`Rerank result count` (Reranked Return Count)	`Top 5 entries` (top 5)	Focuses on the most relevant few pieces of information, reducing model processing burden and improving response quality.
`Temperature (Temperature)`	`0.3`	Suitable for scenarios requiring high accuracy and factual basis, reducing the randomness of model-generated content and ensuring rigor.

Three Common Mistakes

Issue: The Human field is empty in API conversation responses, or conversation records cannot be associated with a user. Reason: The userId or sessionId parameters are missing during API calls, preventing the system from identifying the specific conversation initiator.
Issue: In multi-turn conversations, the model fails to correctly understand a user's follow-up question about a previously mentioned "primary study endpoint." Reason: The maxContext parameter is set too low, truncating information from earlier conversation turns and preventing the model from accessing the complete context.
Issue: The model provides incorrect units or imprecise values when answering questions involving drug dosages or statistical P-values. Reason: Knowledge base document chunking granularity is too large, splitting context containing critical numerical values and units, or the prompt does not explicitly request unit validation.

How to Verify Correct Configuration

Perform multi-turn conversation tests. Verify the model accurately recalls trial design, drug names, or subject characteristics mentioned in previous turns.
Input queries containing specialized abbreviations and medical terms. Check if the model correctly parses them and retrieves relevant document segments from the knowledge base. Also, verify that the similarity scores of retrieved documents meet the expected threshold.
Simulate a user asking about specific data points from a particular clinical report (e.g., CSR). An example query is, "What is the primary endpoint efficacy rate of this drug in Phase III clinical trials?" Verify that the numerical values and units returned by the model match the original text.
Check system logs. Confirm that the userId or sessionId parameters are correctly passed during API calls to ensure the independence of user conversation histories.

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.