Data Characteristics for This Category
Academic promotion documents in the biomedical field originate from clinical study reports, drug inserts, medical guidelines, expert consensus, and published academic papers. These documents update infrequently, typically on a quarterly or semi-annual basis, following drug development progress, clinical trial results, or guideline revisions (e.g., updated inserts after new indications are approved). Document structures are complex, often including charts, references, and specialized terminology. Fields and units are highly standardized. For example, clinical trial data strictly adheres to ICH-GCP guidelines. Dosage units are primarily milligrams (mg), micrograms (µg), and milliliters (mL). Time units are hours (h), days (d), and weeks (wk). Statistical indicators commonly include P-values and confidence intervals (CI). Data may also contain specific disease ICD-10 codes or drug ATC codes; these coding systems are fixed within the documents.
Constraints Imposed by These Characteristics on Multiturn Conversations and Prompts
The professional and rigorous nature of academic promotion documents demands high accuracy in multiturn conversations. Infrequent document updates mean high knowledge base stability, but new content requires careful integration. Complex document structures and extensive specialized terminology require prompt design to consider the depth of semantic understanding in context. Standardized units and fields mean the model must precisely match information during extraction and generation to avoid errors from unit confusion. For example, incorrect dosage units can lead to severe consequences. The ability to trace data sources in conversations is crucial; users may need to inquire about the source literature or clinical data for specific conclusions. Furthermore, due to compliance requirements in academic promotion, the model must avoid any inferences or suggestions beyond the established literature. This directly impacts restrictions on the model's "free play" in prompts.
Configuration Settings
| Configuration Item | Suggested Value | Rationale for This Value |
|---|---|---|
maxContext | 20 turns | Academic promotion conversations often require longer context for logical coherence and complete information. |
Chunk size (Segment Length) | 800–1200 characters | Balances document professionalism, paragraph integrity, and model processing efficiency, preventing semantic fragmentation. |
Recall count (Number of Retrieved Items) | top 5–8 items | Ensures coverage of multiple sources, especially when questions involve several clinical trials or guidelines. |
Similarity threshold (Similarity Threshold) | Calibrated by actual measurement, 0.75 or higher recommended | Ensures precise matching of retrieved content, avoiding interference from irrelevant medical terms. |
Rerank result count (Number of Reranked Items) | 3 items | Focuses on the most relevant key information, reducing the burden on the model to process irrelevant context. |
temperature | 0.1–0.3 | Reduces the risk of model hallucination, ensuring answers are based on original text and comply with regulations. |
Three Common Mistakes
- The conversation displays "No relevant information found" or "Content is empty." This occurs when the number of retrieved items or the similarity threshold is set too strictly, preventing the model from acquiring sufficient context to support an answer.
- AI responses show confusion in dosage or time units, such as misidentifying "mg" as "g." This usually happens when prompts do not emphasize unit precision or when the model's normalization of units is insufficient when processing heterogeneous data from multiple sources.
- When users ask for specific data sources, the AI cannot provide specific literature or report names. This indicates that prompts did not adequately guide the model to attribute sources, or that document metadata (e.g., literature ID) was not effectively embedded during knowledge base construction.
How to Confirm Proper Configuration
- Randomly select 20 sets of real user queries. Check numerical information such as dosage and time in AI responses to ensure unit precision.
- For complex multiturn follow-up questions, evaluate the logical coherence and depth of context understanding in AI responses. Verify that the AI can provide accurate, progressive answers based on previous conversation history.
- Randomly select 10 specific medical concepts or clinical data. Through multiturn conversations, test whether the AI can accurately trace their origin in the original documents (e.g., chapter, table name, or literature ID).
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.