Data Characteristics
Phase II-III clinical trial regulations and Standard Operating Procedure (SOP) documents typically originate from regulatory bodies (e.g., drug administration agencies), internal sponsor guidelines, or clinical research organizations. These documents are updated infrequently, usually a few times per year, in response to regulatory changes or internal process optimizations. Document structures are rigorous, often in PDF format, and include numerous hierarchical headings, numbered lists, tables, and flowcharts. Fields and units are highly specialized, such as dosage units (mg/kg), time points (Tmax, Cmax), statistical indicators (P-value, CI), and disease-specific diagnostic criteria and efficacy evaluation metrics. Documents are generally lengthy, with a single file potentially spanning dozens to hundreds of pages.
Constraints Imposed by These Characteristics on Multi-Turn Conversations and Prompts
The rigor and specialized nature of Phase II-III clinical regulation documents demand that multi-turn conversational systems possess precise semantic parsing capabilities. This prevents misinterpretations of specialized terminology that could lead to incorrect answers. The hierarchical structure and length of documents mean that a single retrieval often cannot cover all relevant information. Therefore, multi-turn conversations are necessary to progressively focus and refine questions. Frequent numbered lists and tabular data require prompt design to guide the model in accurately extracting and summarizing key information, such as regulations in specific sections or parameter ranges in tables. The low update frequency means that knowledge base construction can emphasize deep indexing and version management, reducing the burden of frequent data synchronization. The presence of specialized units requires the model to maintain unit consistency and accuracy in its responses, avoiding errors caused by unit confusion.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 6000 tokens | Ensures sufficient context information in multi-turn conversations to handle complex and specialized question chains. |
segmentLength | 800–1200 characters | Balances semantic integrity of long documents with retrieval efficiency, preventing context loss from excessive splitting. |
recallCount | top 10–15 items | Increases the retrieval scope, improving the probability of hitting key information from vast regulatory documents. |
similarityThreshold | 0.75–0.85 | Ensures the professional relevance of retrieved content, filtering out low-similarity, non-core clauses. |
rerankReturnCount | top 5 items | Further improves the ranking of the most relevant content based on initial retrieval, optimizing model input. |
promptTemplate date format | {{YYYYyearsMMmonthsDDDay}} | Ensures the date format for official documents, facilitating traceability and auditing. |
Three Common Mistakes
- Symptom: The model quotes irrelevant regulations or specialized terms, leading to inaccurate answers. Reason: The
similarityThresholdis set too low, resulting in the retrieval of numerous non-core or peripheral document segments. - Symptom: When users ask about specific operational procedures in a multi-turn conversation, the model fails to provide a complete step-by-step list, giving fragmented answers. Reason: The
segmentLengthis set too small, causing flowcharts or numbered lists to be split into discontinuous segments, making it difficult for the model to integrate them. - Symptom: When multiple AI conversation components are chained in a workflow, the final output includes redundant content from intermediate conversations. Reason: The
promptTemplatedoes not explicitly instruct the model to output only the final result, causing it to return the entire conversation process.
How to Verify Configuration
- Select common complex multi-turn questions from Phase II-III clinical trials. Verify if the model can provide accurate and complete answers within 3-5 turns, citing the correct original regulatory text.
- Randomly select key specialized terms and units from documents. Ask the model for their definitions, scope of application, or conversion relationships. Check the precision of the answers and the correctness of the units.
- Simulate user queries about the effective date or revision history of specific regulatory clauses. Verify if the model can accurately identify and cite the date variables configured in the
promptTemplate, and check if its output format meets expectations.
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.