Multi-Turn Conversations and Prompts for Rehabilitation

Data Characteristics for This Category

Quality documentation for rehabilitation equipment originates from design, development, manufacturing, testing, and post-market surveillance. Documents are typically stored in PDF, Word, and Excel formats. They cover technical requirements, risk analyses, validation reports, instructions, user manuals, and regulatory compliance statements. Document update frequency is relatively low. Updates primarily occur at critical product lifecycle stages, such as design changes, manufacturing process adjustments, or regulatory updates. Document structure is highly standardized, adhering to medical device industry norms like ISO 13485 or GMP. Fields and units are highly specialized. Examples include "safety margin" (percentage), "load capacity" (Newton/N), "protection class" (IPXX), and "mean time between failures" (hours/h). Documents often include technical drawings and complex tabular data.

Constraints Imposed by These Characteristics on Multi-Turn Conversations and Prompts

The standardized structure and specialized nature of rehabilitation equipment quality documentation impose specific requirements on multi-turn conversation accuracy and prompt construction. The large volume of specialized terminology and units in documents requires precise semantic understanding from the model to avoid ambiguity in conversations. A low document update frequency means high knowledge base stability. However, when regulatory updates or product recalls occur, quickly updating the knowledge base and ensuring the conversation system immediately reflects the latest information is critical. Technical drawings and complex tabular data embedded in documents challenge knowledge base preprocessing and RAG (Retrieval Augmented Generation) mechanisms. These non-textual information types must be effectively indexed and retrieved to support deeper question answering. Furthermore, compliance requirements dictate that the conversation system must cite specific document sections or page numbers when answering sensitive questions.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`6`	Rehabilitation equipment-related questions often require a longer context for accurate understanding, balancing performance and effectiveness.
`Chunk size`	`500–800 characters`	Quality document paragraphs are often long and specialized, ensuring individual segments contain enough information.
`Recall count`	`Top 8 entries`	Ensures retrieval of sufficient relevant document fragments to cover complex or multi-faceted questions.
`Similarity threshold`	`0.75`	Guarantees precision of retrieved content, preventing inclusion of irrelevant or low-relevance documents.
`Rerank result count`	`Top 3 entries`	Improves the quality of the final returned results, prioritizing the most relevant document fragments.
`ENABLE_INPUT_GUIDE`	`true`	Guides engineers in asking questions, especially when dealing with complex equipment parameters and regulatory clauses.

Three Common Mistakes

Symptom: The model cannot reiterate xlsx file content from the knowledge base, claiming it cannot read the file. Reason: The knowledge base might not have correctly configured data extraction or indexing strategies when processing structured files like xlsx, preventing effective conversion of data into retrievable text segments.
Symptom: Input guidance and a vocabulary are enabled, but expected question prompts do not appear in the conversation interface. Reason: The "Guided Questions" option in the application's "Prompt and Model" configuration might not be correctly linked to the configured vocabulary or preset question set.
Symptom: In the AI conversation response from an API call, variable B's value is superimposed with the result of the previous variable A. Reason: In workflow design, if the "Context" or "Message History" parameters configured for the AI Chat node are not correctly cleared or isolated, response content between different variables might overlap.

How to Confirm Correct Configuration

Conduct multi-turn simulated conversations for core rehabilitation equipment models or regulatory clauses. Check if the model accurately understands and provides compliant answers, citing correct document sources.
Upload new versions of quality documents containing complex charts and specialized terminology. Test if the conversation system immediately recognizes and answers related questions after the knowledge base update. Check the index status.
Simulate questions from different engineer roles (e.g., design, production, quality inspection). Confirm the conversation system's adaptability and accuracy for different perspectives. Ensure the system_prompt in the AI Chat node effectively guides the model.

Note: The values provided are common starting points. Measure 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.