Multi-turn Conversations and Prompts for Phase II-III

Data Characteristics for this Category

Phase II-III clinical product data originates from clinical trial protocols, investigator brochures, informed consent forms, case report forms (CRFs), clinical study reports (CSRs), and related regulatory submission documents. This data typically exists as a mix of structured (e.g., database records, statistical analysis results) and unstructured (e.g., PDF documents, Word documents, images) formats. Data update frequency is relatively low, primarily occurring during the submission of interim trial reports and the release of final reports. Document content is highly specialized, containing extensive medical terminology, dosage units (e.g., mg/kg, IU), pharmacokinetic parameters, statistical indicators (e.g., P-values, confidence intervals), and complex biomarker names. Document structure is rigorous, often adhering to international guidelines like ICH GCP, with strong correlations between section numbering and content.

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

The highly specialized nature of Phase II-III clinical data requires multi-turn dialogue systems to accurately understand medical terminology and context, preventing errors caused by ambiguity. Low data update frequency means knowledge base construction must pay special attention to historical version management, ensuring that current or specified versions of clinical data are referenced in multi-turn conversations. Complex document structures, including numerous charts and cross-references, demand document parsing capabilities that can effectively extract tabular data and related information, and accurately present them in dialogue. Furthermore, the various units and statistical indicators in clinical data place higher demands on prompt engineering, ensuring the model correctly identifies and uses these units when generating responses, and provides reasonable explanations for statistical results, avoiding misleading information.

Configuration Settings

Configuration Item	Recommended Value	Rationale for this Value
`Chunk size`	`800–1200 characters`	Clinical document paragraphs are often long, containing complete arguments; this avoids context fragmentation.
`Recall count`	`8–12 entries`	Ensures sufficient coverage of relevant clinical trial details in complex question-answering scenarios.
`Similarity threshold`	`0.78–0.85`	Ensures recalled knowledge snippets are highly relevant to the query, filtering out vaguely matched medical terms.
`Rerank result count`	`5 entries`	Further refines recall results, improving the accuracy and relevance of the final answer.
`maxContext`	`8000–12000 tokens`	Accommodates the context length of medical Q&A, supporting in-depth multi-turn conversations and complex reasoning.
`QUERY_REWRITE_MODEL`	`gpt-4o`	Improves the accuracy of complex medical question rewriting, reducing semantic drift.

Three Common Mistakes

Dialogue results show incorrect use of medical terminology or unit confusion. This occurs when prompts do not sufficiently emphasize unit correctness and the contextual semantics of professional terms.
The model "forgets" in multi-turn conversations, failing to link to clinical trial details from previous turns. This happens when the maxContext parameter is set too low, leading to truncation of early dialogue history.
Responses to questions about clinical trial reports are too general or lack critical statistical data. This is due to the inability of document parsing to effectively identify and extract core indicators and values within tables.

How to Verify Proper Configuration

Ask questions targeting key medical terms and dosage units. Verify that the terms and units in the response are used precisely and correctly.
Design complex questions involving three or more turns of contextual association. Validate if the model can accurately reference clinical trial numbers, drug names, or study phases mentioned in earlier turns.
Randomly select clinical documents containing tables and charts. Ask questions about key data within them. Check if the response accurately presents the numerical values and units from the tables.
Compare the similarity scores in the retrieval results section of the FastGPT debugging interface. Ensure that high-similarity relevant document snippets are recalled for specialized queries.

The values provided are common starting points and should be measured against the reader's 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.