Multiturn Conversation and Prompts for Structured Analysis

Data Characteristics for This Category

Clinical decision support systems process data primarily from clinical trial reports, drug inserts, medical guidelines, patient records, and biomedical research papers. Document update frequencies vary. Drug inserts and medical guidelines typically revise annually or based on regulatory requirements. Clinical trial data might update in real-time. Document structures are often standardized. Clinical trial reports commonly include sections like abstract, methods, results, and discussion. Drug inserts have fixed fields such as indications, dosage and administration, and adverse reactions. Medical guidelines usually present evidence-based recommendation levels and suggestions. Fields and units involve dosage (e.g., mg/kg), frequency (e.g., QD, BID), treatment duration (e.g., weeks, months), and various biomarkers (e.g., ng/mL, mmol/L) and laboratory indicators.

Constraints Imposed by These Characteristics on Multiturn Conversation and Prompts

The structured nature of clinical decision support documents places specific demands on multiturn conversation and prompt design. First, the authoritative and rigorous nature of data sources requires the conversation system to precisely trace information when citing, avoiding vague explanations. Prompt design must guide the model to explicitly state the source document or section. Second, varying update frequencies mean the system needs version management capabilities to ensure conversations always rely on the latest or specified version of clinical data. In multiturn conversations, users might inquire about the guideline version supporting a conclusion. Third, the complex document structure and specialized fields require prompts to effectively extract key information. Examples include precisely locating efficacy data for a specific drug from a clinical trial report or extracting contraindication lists from a drug insert. Strict requirements for units like dosage and frequency necessitate prompts that guide the model in unit conversion or validation, preventing decision errors due to unit confusion.

Configuration Settings

Configuration Item	Suggested Value	Rationale for This Value
`maxContext`	`8000 tokens`	Clinical decisions involve complex logic and multiple information sources, requiring a long context window for coherent and in-depth conversations.
`Chunk size` (Segment Length)	`500 characters` (characters)	Clinical documents have high information density. Overly long segments might lead to redundancy, while overly short ones might break semantic integrity.
`Recall count` (Recall Count)	`Top 8 entries` (top 8)	Ensures coverage of multiple relevant document snippets while avoiding interference from too much irrelevant information.
`Similarity threshold` (Similarity Threshold)	Calibrated by actual measurement	Requires balancing recall and accuracy to avoid missing critical information or introducing noise.
`Rerank result count` (Rerank Return Count)	`Top 5 entries` (top 5)	Further refines the most relevant information based on initial recall, improving answer quality.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Processing large clinical trial reports or multi-page medical guidelines can be time-consuming; this ensures complete parsing.

Three Common Mistakes

Unit errors in conversations about drug dosage or frequency, such as confusing mg with g. This happens when prompts do not explicitly emphasize unit validation.
After a user query, the system provides only a general answer without citing specific documents or sections. This occurs when document parsing fails to retain sufficient metadata and structural information.
AI conversations in the workflow cannot correctly handle file links, leading to missing conversation content. This happens when the file link parameter is not correctly configured or file access permissions are insufficient.

How to Confirm Correct Configuration

Conduct multiturn conversation tests to verify the system can accurately cite specific medical guideline version numbers and drug insert sections.
Input queries containing critical information like dosage and frequency. Check if the model's output includes correct units and can perform simple unit conversions or provide prompts.
Upload large clinical trial reports. Observe if the system can complete parsing within the PARSE_FILE_TIMEOUT_SECONDS duration and successfully recall key data from the report in conversations.

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.