Data Characteristics
Data for cardiovascular intervention procedures and standard operating procedures (SOPs) comes from internal medical institution regulations, national and industry health commission clinical guidelines, and medical device manufacturer product manuals. These documents are typically PDFs, Word files, or scanned images. Content covers surgical procedures, device usage specifications, infection control requirements, and emergency plans. Update frequency is stable; major policies or guidelines are revised annually or every few years. Hospital internal SOPs are fine-tuned periodically based on clinical practice and equipment updates. Document structure is often chapter-based, with many specialized terms, flowcharts, tables, and diagrams. Fields and units include surgical time (minutes), device models (e.g., mm), drug dosages (e.g., mg), and pressure units (e.g., mmHg). Many abbreviations and specific codes are present.
Constraints on Multiturn Conversation and Prompts
The specialized and structured nature of cardiovascular intervention procedure documents imposes specific requirements on the accuracy of multiturn conversations and prompt design. First, the extensive medical terminology and abbreviations in the documents require the model to effectively identify and link user queries to corresponding knowledge base content, preventing semantic drift due to inaccurate recognition of professional vocabulary. Second, the contextual relationships of flowcharts and tabular information may be weakened after text conversion. This demands stronger logical reasoning from the model when processing queries about operational steps or parameter comparisons. Furthermore, while document update frequency is not high, any revision can impact clinical decisions. The conversation system must provide answers based on the latest knowledge version and accurately respond when users ask about the "latest version regulations." In multiturn conversations, users may progress from a general question to specific device models or operational details. Prompt design must guide the model to maintain conversational coherence and precisely locate relevant clauses within the extensive regulatory framework.
Configuration Strategy
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 2048 tokens | Balances complex question context with model processing efficiency, preventing truncation or performance degradation from overly long inputs. |
Chunk size | 800 characters | Ensures each segment contains sufficient semantic information while avoiding overly long paragraphs that dilute key information, aiding RAG recall. |
Recall count | 5 items | Reduces interference from irrelevant information while ensuring coverage, improving answer precision and preventing model overload. |
Similarity threshold | 0.75 | For highly specialized and terminologically dense documents, a higher threshold ensures strong relevance of recalled content. |
Rerank result count | 3 items | Further refines recall results, focusing on the top few most relevant items, enhancing the quality and conciseness of the final answer. |
temperature | 0.3 | Reduces the randomness of model-generated answers, ensuring responses based on regulations are more rigorous, accurate, and less prone to hallucination. |
Common Pitfalls
- Phenomenon: A user asks about the steps for a specific device model, but the conversation model provides a generic answer lacking specific procedural details. Reason: The knowledge base segmentation strategy is inadequate, causing text containing complete procedures to be split, or too few recall items to cover all relevant paragraphs.
- Phenomenon: A user asks about the latest revision date for an infection control standard, and the model cannot provide it or gives incorrect information. Reason: The knowledge base does not include document version control information, or timestamp metadata was not effectively extracted and indexed during document processing.
- Phenomenon: A user inputs "how to treat cardiogenic shock," and the model responds with emergency measures for other diseases. Reason: The prompt failed to effectively guide the model to focus on the specific disease, or the knowledge base contains many similar but different emergency guidelines, leading to semantic confusion.
Verification Steps
- Select representative complex multiturn conversation scenarios within cardiovascular intervention. Simulate user query paths from macroscopic to microscopic. Observe whether the model maintains contextual coherence and provides logically clear, accurate answers.
- For frequently updated policies or guidelines in the knowledge base, verify whether the model can accurately cite and differentiate information from different versions when asked about "latest regulations" or "revised content."
- Spot-check documents containing flowcharts or tables. Formulate relevant questions and verify if the model's output steps or parameters exactly match the original document, paying attention to the accuracy of units and values.
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.