Data Characteristics
Complaint ticket data in the biomedical sector originates from patient feedback, adverse event reports, product quality issues, and service dispute resolution processes. This data combines structured and unstructured formats. Structured data includes ticket ID, patient ID, product batch number, complaint type code, processing status, and handler. Unstructured data consists of detailed patient descriptions, customer service communication logs, investigation reports, and expert diagnostic opinions. Content length varies from hundreds to thousands of characters, involving specialized medical terminology, colloquialisms, and emotional language. Data updates occur in real-time or near real-time, continuously increasing with new ticket creation and processing. Document structures may include attachments like images or scanned medical records.
Constraints on Multi-Turn Conversations and Prompts
The large volume of unstructured text in complaint ticket data makes accurate key information extraction fundamental for multi-turn conversations. Patient descriptions mix specialized terms with everyday language, requiring the model to possess strong semantic understanding to differentiate symptoms, diagnoses, and emotional expressions. Real-time data updates necessitate rapid synchronization of the knowledge base with the latest processing progress and solutions to avoid providing outdated information. Multi-turn conversations must support users in tracing and linking historical tickets, such as querying all complaints for a specific product batch. This relies on precise matching and retrieval of structured fields like ticket ID and product batch number. Furthermore, processing may involve sensitive patient health information, imposing strict requirements on data security and privacy, which impacts data preprocessing and model training.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 8 | Ensures the model reviews enough historical turns in multi-turn conversations to maintain coherence and understand the complaint context. |
Chunk size (Segment Length) | 500-800 characters (characters) | Balances text block integrity with model processing efficiency. Avoids diluting key information with overly long text or losing semantics with overly short text. |
Recall count (Recall Count) | Top 10-15 entries (top 10-15 items) | Increases the probability of retrieving relevant information from a vast ticket knowledge base, covering various complaint scenarios and solutions. |
Similarity threshold (Similarity Threshold) | Calibrate by actual measurement | Balances recall and accuracy based on the semantic similarity distribution of actual complaint ticket text, avoiding interference from irrelevant information. |
Rerank result count (Rerank Return Count) | Top 5 entries (top 5 items) | Selects the most relevant content from recall results, improving the quality and specificity of the final answer and reducing model hallucinations. |
Prompt Max Length | 1024 characters (characters) | Provides sufficient space for complex complaint descriptions, historical context, and desired output formats, ensuring complete instructions. |
Common Pitfalls
- "401 No auth credentials found" errors in conversations typically indicate incorrect or expired API key configuration.
- The model fails to reference recently updated ticket processing statuses because the knowledge base synchronization mechanism did not trigger promptly or index updates experienced delays.
- The model does not return relevant records when users inquire about the complaint history of a specific product batch. This may occur if key entities like product batch numbers were not adequately extracted or indexed during knowledge base embedding.
Verification Steps
- Randomly select 10 real complaint cases with multi-turn interactions. Test if the smart customer service maintains conversational context and provides accurate solutions.
- Verify the latest 50 ticket processing updates uploaded to the knowledge base. Confirm they are correctly referenced in the smart customer service's Q&A.
- Choose 5 complaint scenarios involving specialized medical terminology. Check if the model accurately understands and provides professional, error-free responses while avoiding sensitive information disclosure.
- Simulate user queries to retrieve historical tickets for specific products, batches, or complaint types. Evaluate the accuracy and completeness of the retrieval results.
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.