Data Characteristics for This Category
Rehabilitation equipment data comes from various sources, including product manuals, technical white papers, clinical application guidelines, user manuals, maintenance specifications, and medical device registration certificates. These documents are often in PDF, Word, or structured database formats. Product updates are relatively stable, with new versions or improved models typically released annually, though software components might see quarterly updates. Document structures are rigorous, containing extensive technical jargon, device parameters (e.g., power, frequency, treatment modes), scope of application, contraindications, operating procedures, and troubleshooting guides. Fields often include Unique Device Identifiers (UDI), batch numbers, serial numbers, and product models. Units encompass the International System of Units (SI) and common medical domain units (e.g., mmHg, Joules).
Constraints Imposed by These Characteristics on Multi-turn Conversations and Prompts
The specialized and rigorous nature of rehabilitation equipment demands that the dialogue system accurately understands technical terms and device parameters in user queries. Document update cycles dictate the knowledge base synchronization frequency, ensuring information is always based on the latest product versions. Due to complex document structures, which include numerous charts and tables, traditional text segmentation might lead to critical information loss, requiring more refined text preprocessing. In multi-turn conversations, users might progressively inquire about device functionalities, compatibility, or troubleshooting steps. The system needs to maintain contextual coherence and adjust retrieval strategies based on user feedback. Furthermore, consultations involving medical devices demand extremely high accuracy; incorrect or ambiguous responses can have severe consequences. Therefore, prompt design must emphasize fact-checking and clear identification of information sources.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Max Context Window | 32k tokens | Accommodates complex device parameters, clinical cases, and troubleshooting processes in multi-turn conversations, ensuring contextual completeness. |
Chunk Size | 500 characters | Prevents semantic dispersion due to overly long chunks while retaining sufficient contextual information to understand technical terms and parameters. |
Recall Count | 10 | Increases the coverage of retrieved documents, improving the probability of finding relevant information in complex queries, especially when multiple product features intersect. |
Similarity Threshold | 0.75 | Ensures recalled results are highly relevant to the query, filtering out low-quality or generic matches to enhance professionalism. |
Rerank Return Count | 5 | Selects the most relevant document snippets, reducing the burden on the model from processing irrelevant information and improving response accuracy and efficiency. |
Prompt Template | Include "As a professional rehabilitation equipment engineer, based on the provided materials, rigorously answer user questions about device models, technical parameters, operating procedures, or troubleshooting. If information is insufficient, clearly state it." | Guides the model to respond professionally and rigorously, emphasizing information sources and how to handle insufficient information to mitigate potential risks. |
Three Common Pitfalls
- Symptom: AI responses include functional descriptions or parameters that do not match the device model. Reason: Document chunks from different models in the knowledge base overlap, causing the model to incorrectly attribute characteristics of other models to the currently queried model.
- Symptom: When a user asks about an operating procedure, the AI response is disjointed or misses critical steps. Reason: Original documents might present operating procedures primarily through diagrams or split them across multiple sections. Text segmentation might fail to capture the entire process.
- Symptom: AI refuses to answer questions about device compatibility or accessory selection, stating "information insufficient." Reason: The prompt does not explicitly guide the AI on product compatibility or accessory inquiries, or relevant knowledge points are not adequately indexed.
How to Verify Configuration
- Pose multi-turn in-depth inquiries covering parameters, operations, and troubleshooting for typical rehabilitation equipment models. Verify the accuracy and completeness of responses.
- Simulate users progressively refining questions in a conversation. Observe if the AI maintains contextual coherence and adjusts its responses based on new information.
- Intentionally introduce incorrect product models or non-standard terminology. Check if the AI can identify and guide the user to correct them, or clearly state missing information.
- Evaluate whether AI responses explicitly cite information sources from the knowledge base, especially for critical technical parameters or safety guidelines.
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.