Multi-turn Conversations and Prompts for Structured Analysis

Data Characteristics for this Category

Phase I clinical research data primarily originates from subject clinical observation records, adverse event reports, laboratory test results, pharmacokinetic (PK) data, and pharmacodynamic (PD) data. This data exists in both structured formats (e.g., CRF tables, EDC system export files) and unstructured formats (e.g., investigator notes, imaging reports, pathology reports). Document update frequency is high, especially during ongoing studies, with subject data entered in real-time or periodically. Document structures are diverse, including study protocols, informed consent forms, case report forms (CRFs), medical imaging reports, ECG reports, complete blood count, and biochemical test reports. Fields involve dosage, administration route, vital signs, adverse event names, severity, occurrence time, and mitigation measures. Units include mg, ml, mmol/L, ng/mL, mmHg, and °C. Conversion relationships may exist between units in different fields.

Constraints Imposed by these Characteristics on "Multi-turn Conversations and Prompts"

The multi-source heterogeneity of Phase I clinical data requires the dialogue system to have strong semantic understanding and entity recognition capabilities. This ensures accurate extraction of key information from unstructured text and its association with structured data. High update frequency means the knowledge base needs to support efficient incremental updates and version management, ensuring the timeliness of conversation content. Complex document structures and specialized terminology demand high-quality prompt design. Prompts must be carefully constructed to guide the model in understanding context, identifying medical entities, and recognizing logical relationships. For example, adverse event reports often contain free-text descriptions; the model must identify drug-relatedness and severity from these. PK/PD data involves numerous values and units; the dialogue system must handle numerical comparisons, trend analysis, and unit conversions. Additionally, in multi-turn conversations, users may ask follow-up questions about the specific occurrence time or treatment measures of an adverse event. This requires the system to maintain conversational context and retrieve and integrate information from different documents.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`8192 token`	Phase I clinical documents are complex, requiring a longer context window for conversational coherence.
`Chunk size`	`800–1200 characters`	Balances semantic completeness and retrieval efficiency, avoiding the splitting of important medical concepts.
`Recall count`	`Top 8–12 entries`	Ensures coverage of multi-source heterogeneous Phase I clinical data, improving information recall rate.
`Similarity threshold`	`0.78–0.85`	Given the specialized terminology, this threshold is raised to filter out irrelevant retrieval results.
`Prompt Template`	Calibrated by testing	Must include instructions for entity recognition, relationship extraction, and numerical comparison to guide the model in Phase I clinical data analysis.
`Streaming Output`	Enabled	Improves user experience, especially reducing perceived wait times for complex queries.

Three Common Mistakes

"Unauthorized" or "Insufficient Permissions" prompts in a conversation may occur if the API Key's user role lacks access to the corresponding knowledge base, or if the API Key itself has expired.
If the model's answers do not cite any knowledge base content, even after increasing Recall count, the model may "hallucinate." This can happen if Similarity threshold is set too high, causing many retrieved documents to fail the citation standard.
In multi-turn conversations, if the model cannot accurately associate subject information mentioned in previous turns, leading to repetitive questioning or generic answers, the maxContext may be too short, truncating historical conversation information.

How to Verify Configuration

Simulate multiple multi-turn conversations in various Phase I clinical research scenarios. Check if the model accurately understands and answers questions about drug dosage, adverse events, and laboratory indicators, and if it can trace historical conversation context.
Verify the knowledge base passages cited by the model in its answers. Confirm that the cited content is highly relevant to the question and that corresponding evidence can be found in the original documents. This assesses the effectiveness of Similarity threshold and Recall count.
After a knowledge base update, check if the model can immediately access and utilize the latest Phase I clinical data for conversations. This verifies the knowledge base's real-time synchronization capability.

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.