Multi-turn Conversation and Prompts for Cardiovascular

Data Characteristics

Cardiovascular intervention medical device registration documents draw from diverse sources. These include clinical trial reports, technical requirements, risk management reports, product specifications, and user manuals. Most documents are in PDF or Word format. Some data, such as clinical trial results or material composition lists, are presented in Excel tables. Data update frequency correlates with product development cycles and regulatory revisions, typically occurring during product upgrades, expanded indications, or new regulatory requirements. Document structures are rigorous, adhering to specific templates from the National Medical Products Administration (NMPA) or international medical device regulatory bodies (e.g., FDA, CE). They contain extensive specialized terminology, abbreviations, and units of measurement (e.g., millimeters, milligrams, joules, PSI). Field naming is standardized, but specific fields exist for different product subcategories (e.g., stents, catheters, balloons).

Constraints Imposed by These Characteristics on Multi-turn Conversation and Prompts

The specialized and standardized nature of cardiovascular intervention data places specific demands on multi-turn conversation and prompt design. The large volume of specialized terms and abbreviations requires the model to have strong semantic understanding, accurately identifying and associating context to avoid misunderstandings caused by lexical ambiguity. The rigorous document structure means the conversation system must parse complex hierarchical relationships, for example, tracing clinical trial data back to corresponding trial protocols and statistical analysis methods. The uncertain data update frequency requires the knowledge base to quickly ingest and index new document versions, ensuring real-time accuracy of conversation results. Additionally, the presence of specific fields for different product subcategories means prompt design must balance generality and specificity. This ensures effective guidance for the model to extract relevant information during cross-product queries and precise targeting of key parameters during specific product queries.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size`	`800–1200 characters`	Balances semantic completeness and recall efficiency. Avoids excessive truncation of specialized terms and short sentences.
`Chunk overlap`	`100–200 characters`	Ensures contextual continuity, especially for paragraphs with complex logic and argumentation.
`Recall count`	`Top 5–8 entries`	Registration document queries often require multi-faceted information support. Increasing recall quantity aids comprehensiveness.
`Similarity threshold`	Calibrate via empirical testing	Balances recall and precision. Avoids interference from irrelevant information while ensuring no critical information is missed.
`Rerank result count`	`Top 3–5 entries`	Further refines recall results, improving the quality of context presented to the model.
`maxContext`	`3000–4000 token`	Ensures the model can process long contexts containing multiple document snippets and conversation history, understanding complex declaration requirements.

Common Pitfalls

The model's responses show data truncation or incomplete logic. This occurs when the maxContext parameter is set too low, preventing complete loading of all relevant knowledge fragments.
The model returns a large volume of irrelevant or low-relevance results after a user query. This happens when the Similarity threshold (similarity threshold) is set too low, failing to effectively filter noise information.
Queries for performance parameters of specific device models fail. This is because the knowledge base did not effectively parse and index tabular data containing key fields like model and batch during import, preventing accurate retrieval by the model.

How to Verify Configuration

For typical complex queries, verify that the model's output covers all key information points. Check that cited original passages are complete and accurate.
Compare retrieval results under different Recall count (recall quantity) and Similarity threshold (similarity threshold) configurations. Assess whether recall and precision meet expectations.
Simulate specific questions for different cardiovascular intervention device subcategories. Check if the model can accurately identify and extract information from corresponding unique fields.

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.