Multi-Turn Conversations and Prompts for Cardiovascular R&D

Data Characteristics

Cardiovascular R&D documents include clinical trial reports, drug mechanism of action studies, biomarker analyses, disease model data, and regulatory submission materials. These documents are often in PDF, Word, or Excel formats. They have complex structures and contain extensive specialized terminology, dosage units (e.g., mg/kg, µmol/L), measurement units (e.g., mmHg, bpm), and charts. Data updates frequently, especially during new drug development, with continuous generation of clinical trial interim reports and adverse event monitoring data. Documents often describe electrocardiogram (ECG) data, interpret imaging reports (e.g., echocardiography, coronary angiography), and detail cardiovascular disease-related mutations from gene sequencing data.

Constraints on Multi-Turn Conversations and Prompts

The specialized and data-intensive nature of cardiovascular R&D documents places specific demands on multi-turn conversation and prompt design. Professional terms like drug names, gene loci, disease diagnostic criteria, and clinical endpoints require the model to accurately understand domain-specific terminology. Numerical information such as dosages, frequencies, and effect values needs precise extraction and comparison to avoid misjudgments due to unit or dimension confusion. In multi-turn conversations, users may progressively refine queries, for example, moving from "efficacy of drug X for hypertension" to "incidence of cardiovascular adverse events of drug X in patients with specific genotypes." This requires the dialogue system to maintain context and perform logical reasoning. Prompt design must guide the model to focus on key information, such as clinical trial inclusion/exclusion criteria and statistically significant results, while filtering out redundant experimental method descriptions.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Segment Length)	`800–1200 characters`	Ensures each segment contains sufficient context while preventing information overload from excessive length.
`Recall count` (Recall Count)	`Top 5`	Balances retrieval efficiency and relevance. Cardiovascular documents are highly specialized, and the top few results usually cover core information.
`Similarity threshold` (Similarity Threshold)	`0.78–0.85`	For precise matching of specialized terminology, reducing the risk of recalling irrelevant content.
`Rerank result count` (Rerank Return Count)	`Top 3`	Further refines search results, prioritizing the most relevant cardiovascular data points for the user's query.
`maxContext`	`4096 tokens`	Accommodates the context length requirements for complex queries and multi-turn conversations in the cardiovascular domain.
`maxResponse`	`1024 tokens`	Ensures the model can provide detailed and structured cardiovascular-related answers, including key data.

Common Pitfalls

Prompts do not explicitly specify units or fields, causing the model to confuse dosage or biomarker data extraction, for example, equating "mg" with "µg".
Knowledge base API calls return authorization failures, possibly due to incorrect API_KEY or APP_ID configuration, or an incorrect Authorization header format.
In multi-turn conversations, subsequent user questions disconnect from the context of previous cardiovascular disease queries, preventing the model from accurately understanding intent and leading to irrelevant answers.
After increasing Recall count (Recall Count), the model's response does not cite any knowledge base content. This may stem from prompts not effectively guiding the model to use {{cited content}} ({{citation content}}) or {{knowledge base content}} ({{knowledge base content}}) placeholders.

Verification Steps

Conduct a series of queries containing cardiovascular professional terminology. Check if the model accurately extracts and presents key numerical values such as dosages, clinical endpoints, and adverse events. Verify that the extracted units match those in the original documents.
Simulate multi-turn conversation scenarios, progressively refining a query about a specific cardiovascular drug. Observe if the model continuously understands context and provides more precise cardiovascular-related information in subsequent answers based on previous turns.
Check if the model's answers include citation links or paragraph IDs from the knowledge base. Click to verify that these citations point to parts of the original document closely related to the answer content.
Check system logs to confirm that truncation or warning mechanisms are correctly triggered when maxContext or maxResponse limits are reached, and that the truncated answers maintain the integrity of cardiovascular information.

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.