Multi-turn Conversations and Prompts for After-Sales and

Data Characteristics for This Category

Biomedical after-sales and warranty data primarily originates from product manuals, repair guides, user feedback records, technical support documents, and regulatory compliance files. This data has a relatively low update frequency, typically updating with product iterations or regulatory changes. Documents are highly structured, containing numerous fields such as product model, batch, serial number, fault code, diagnostic procedures, operating steps, parts lists, and warranty terms. The data often involves medical terminology, equipment parameters (e.g., voltage V, current A, temperature °C, pressure kPa, dosage mg), date formats (e.g., YYYY-MM-DD), and specific encoding rules. Some data may exist as scanned documents or images, requiring OCR processing.

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

Highly structured data requires precise field extraction and indexing during knowledge base construction to avoid ambiguous matching. Low update frequency leads to relatively stable knowledge base content, but robust historical version management and update mechanisms are still necessary to accommodate product lifecycle changes. Extensive specialized terminology and parameters demand strong entity recognition capabilities. Prompt design must guide the model to correctly interpret this information. Image-format data increases pre-processing complexity, potentially affecting file upload and knowledge retrieval efficiency. Conversations often involve complex logic such as equipment troubleshooting and operational guidance, requiring multi-turn conversations to possess state tracking and logical reasoning abilities. Prompts must explicitly guide the model to provide step-by-step responses and handle potentially non-standard user descriptions.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale for Recommendation
`maxContext`	`1024` tokens	Ensures conversation history is sufficient to cover complex troubleshooting processes while controlling computational costs.
`Chunk size` (Segment Length)	`300` characters	Balances semantic completeness and fragment retrieval efficiency, adapting to the paragraph structure of technical documents.
`Recall count` (Number of Retrieved Items)	`5-8` items	Covers relevant knowledge points, avoids missing critical information, and limits redundancy.
`Similarity threshold` (Similarity Threshold)	Calibrate based on actual measurements	Adjusts based on the specific dataset, balancing retrieval precision and recall rate, avoiding "no answer found."
`Rerank result count` (Number of Reranked Items)	`3` items	Prioritizes the most relevant knowledge, enhancing user experience and reducing the model's processing burden.
`UPLOAD_FILE_MAX_SIZE`	`50` MB	Accommodates the upload requirements for large product manuals, repair drawings, and other files, preventing `503` errors.

Three Common Mistakes

After uploading scanned documents or images in a conversation, the model responds with "no answer found." This occurs because the knowledge base failed to correctly recognize text information in the image, or the image address was not effectively indexed.
When a user continuously asks warranty questions about different product models, the model's responses become confused or jump between topics. This happens because the maxContext parameter is set too low, leading to the loss of historical information in multi-turn conversations and an inability to maintain contextual coherence.
When making API calls, rapidly sending multiple questions causes some request responses to be delayed or stuck. This is due to insufficient backend capacity for handling concurrent requests, or resource competition between requests.

How to Verify Configuration

Simulate user questions to verify if the model can accurately identify key entities such as product model, batch, and fault code, and provide correct after-sales or warranty information.
Upload documents in different formats (PDF, image) to check if the knowledge base can correctly parse the content and successfully retrieve relevant information in the conversation, verifying image content retrieval.
Test complex multi-turn conversation scenarios, such as fault diagnosis procedures, to confirm if the model can maintain contextual coherence and guide the user step-by-step through the consultation, verifying the effectiveness of the maxContext parameter.
Perform stress tests via the API to observe response times and error rates under concurrent requests, evaluate system stability, and adjust parameters such as UPLOAD_FILE_MAX_SIZE based on actual load conditions.

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.