Data Characteristics
Surgical robot quality documentation originates from design, development, manufacturing, verification, testing, clinical application, and post-market surveillance. These documents include design inputs/outputs, risk management reports, software verification reports, hardware test reports, clinical evaluation reports, user manuals, maintenance manuals, regulatory compliance declarations, and adverse event reports. Documents update frequently, especially with product iterations, software upgrades, or regulatory changes. Document structures are complex, containing specialized terminology, diagrams, flowcharts, and data tables. Fields and units are highly specialized. For example, software verification reports may include "Test Case ID," "Expected Result," "Actual Result," and "Defect Level." Hardware test reports may contain "Precision Deviation (mm)," "Repeatability (mm)," and "Load Capacity (N)."
Constraints on Multi-Turn Conversations and Prompts
The specialized and complex nature of surgical robot quality documentation demands high accuracy and depth in multi-turn conversations. Frequent document updates require continuous knowledge base synchronization to ensure timely conversation results. Complex document structures and cross-references necessitate that the conversation system understands context and performs deep logical reasoning. The abundance of specialized terminology and standard units means prompt design must be precise, avoid ambiguity, and guide the model to query specific technical details. For example, when a user asks about "robotic arm repeatability," the system should understand this refers to a key performance indicator for a specific surgical robot model. It should provide data from relevant test reports and even ask the user about the specific test conditions for repeatability.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8 | Manages complex logical relationships and contextual dependencies in multi-turn conversations, ensuring information completeness. |
Chunk size (Segment Length) | 800–1200 characters (characters) | Balances document semantic integrity with retrieval efficiency, accommodating long sentences and paragraphs common in technical documents. |
Recall count (Retrieval Count) | Top 5 entries (top 5) | Ensures coverage of relevant information from various angles, improving answer comprehensiveness. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements | Accurately matches specialized terminology and technical specifications, avoiding interference from irrelevant information. |
Rerank result count (Reranked Return Count) | 3 | Focuses on the most relevant key information, reduces model processing burden, and improves response speed. |
Parsing Timeout | 600 seconds (seconds) | Handles parsing of large PDF or image-heavy documents, preventing timeouts due to file complexity. |
Three Common Mistakes
- Symptom: Conversation results contain parameters or performance indicators inconsistent with the current surgical robot model. Reason: The prompt did not explicitly limit the query scope, or the model failed to effectively identify product model information in the documents.
- Symptom: A user asks for specific data from a test report, and the conversation system returns "No relevant information found." Reason: Document segmentation granularity was too large, diluting key data points, or the retrieval strategy failed to precisely locate paragraphs containing specific values.
- Symptom: In a multi-turn conversation, the output of the first query does not serve as effective input for subsequent queries, leading to context breaks. Reason: The
maxContextparameter was set too low, or the prompt design failed to effectively guide the model to use previous conversation results for reasoning.
How to Confirm Proper Configuration
- Conduct multi-turn queries on core performance indicators for different surgical robot models. Verify the conversation system's ability to accurately differentiate and provide corresponding data.
- Randomly select multiple quality documents containing charts and specialized terminology. Test the conversation system's ability to parse and explain key information within them.
- Simulate user follow-up questions on specific regulatory compliance or risk management details. Check whether the conversation system maintains contextual coherence and provides logically clear answers.
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.