Data Characteristics
Cardiovascular intervention product data originates from medical device manufacturers' product manuals, technical specifications, clinical trial reports, industry standards, and post-market surveillance data. This data updates infrequently, typically with product iterations or regulatory changes. Product manuals are often in PDF format, containing detailed product parameters, operating instructions, indications, contraindications, and risk warnings. Technical specifications delve into material composition, manufacturing processes, and performance indicators, often presented as structured text or tables. Clinical trial reports include extensive experimental designs, statistical data, and results analysis. Fields and units are highly specialized, such as "stent diameter" (mm), "balloon length" (mm), "catheter sheath inner diameter" (Fr), and "release pressure" (atm), requiring high precision for numerical values and unit consistency.
Constraints Imposed by Data Characteristics on Multiturn Conversations and Prompts
The specialized and rigorous nature of cardiovascular intervention product data demands that multiturn dialogue systems accurately identify professional terminology and numerical units when understanding user queries, avoiding semantic ambiguity. The low frequency of product updates means that knowledge base construction should focus on managing and retrieving historical data versions to ensure timely and accurate answers. The presence of numerous structured and semi-structured documents dictates that knowledge retrieval must balance text matching with structured information extraction. For example, if a user asks for the "maximum passage diameter" of a specific stent model, the system cannot rely solely on keyword matching. It must also understand the specific location and meaning of "passage diameter" within the product specification table. The precision requirements for numerical parameters also limit the freedom of generative responses, necessitating greater reliance on extracting answers directly from the source text.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 10 turns | Ensures multiturn conversations cover complex product specification comparisons and selections. |
Chunk size (Segment Length) | 400–600 characters | Balances the completeness of professional terminology with retrieval efficiency. |
Recall count (Retrieval Count) | top 8–12 entries | Covers more relevant technical documents and clinical reports. |
Similarity threshold (Similarity Threshold) | 0.78–0.85 | Ensures the professionalism and accuracy of retrieved content. |
Rerank result count (Reranked Return Count) | top 5 entries | Focuses on the most relevant product specifications and operating instructions. |
promptTemplate | Calibrate by measurement | Guides the model to focus on key information such as product parameters, indications, and contraindications. |
Common Pitfalls
- Phenomenon: The AI conversation returns product models or parameters that are confused. Reason: The knowledge base segmentation strategy is inappropriate, leading to independent information for different models or specifications being incorrectly merged or split.
- Phenomenon: The AI cannot provide a comprehensive answer when users inquire about specific product risks. Reason: The prompt failed to adequately guide the model to extract risk information from clinical trial reports and post-market surveillance data.
- Phenomenon: In a multiturn conversation, the system cannot delve deeper into specific product details based on follow-up questions from the previous turn. Reason:
maxContextis set too low, leading to loss of historical conversation information and inability to maintain contextual coherence.
Validation Steps
- Design queries containing multiple professional terms and numerical parameters for different product models. Verify the system's ability to accurately identify and return corresponding product specifications.
- Simulate multiturn conversations in various scenarios (e.g., product selection, operational guidance, risk consultation). Check the context retention and answer relevance for each conversation.
- Randomly select specific product documents from the knowledge base. Construct questions targeting their indications, contraindications, and usage methods. Check the accuracy and completeness of the AI's responses.
The values given 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.