Data Characteristics in this Domain
Quality documents in stem cell therapy originate from laboratory research reports, clinical trial protocols, production batch records, quality control standard operating procedures (SOPs), and regulatory compliance files. These documents have a relatively low update frequency, primarily changing when research milestones are reached, clinical trial phases shift, production processes optimize, or regulations update. Document structures are highly standardized, typically including an introduction, objective, scope, responsibilities, operating procedures, record forms, and references. Common fields include batch number, production date, expiration date, cell count, viability, purity, differentiation potential, sterility test results, and endotoxin levels. Units strictly adhere to international standards, such as cells/mL, %, EU/mL, and CFU/mL, often accompanied by specific testing methods and judgment criteria.
Constraints on Multi-Turn Conversations and Prompts
The standardized structure and low update frequency of stem cell therapy quality documents demand high accuracy for knowledge recall and traceability in multi-turn conversations. Precise field and unit information requires prompt design to avoid ambiguous statements. This ensures the model accurately understands user intent and extracts relevant data, for example, distinguishing cell viability data from different batches. The extensive specialized terminology and abbreviations in these documents require the model to have strong semantic understanding. It must connect context to avoid information discrepancies due to ambiguous terms. Furthermore, the strictness of regulatory compliance files means conversation results must be highly accurate and verifiable. Any misinterpretation could lead to severe compliance risks. Therefore, multi-turn conversation flow design should focus on guiding users to provide sufficient contextual information and using follow-up questions to calibrate user intent, ensuring the precision and reliability of the final answer.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 8192 token | Ensures the capacity to handle complex multi-turn conversation history and longer document segments, preventing information loss. |
Chunk size | 500-800 characters | Stem cell document paragraphs have clear structures; this length helps maintain semantic integrity and reduces misinterpretation. |
Recall count | Top 10 entries | Given the dense information in specialized documents, increasing the number of recalled items enhances the probability of selecting highly relevant segments. |
Similarity threshold | 0.75-0.85 | A high threshold ensures recalled document segments are highly relevant to the query, reducing interference from irrelevant information. |
Rerank result count | Top 5 entries | After re-ranking, a smaller number of the most relevant segments are selected, improving model processing efficiency and answer quality. |
TEMPERATURE | 0.3-0.5 | Reduces model creativity, ensuring answers are based on original text, minimizing hallucinations, and meeting the stringent requirements of quality documentation. |
Common Pitfalls
- A "cell count result missing" message in a conversation may occur if the prompt does not explicitly specify the batch number or detection method, preventing the model from precisely matching information in the document.
- The model may provide contradictory answers to compliance questions if critical regulatory clauses and their explanations are separated during document chunking, leading to a lack of complete context for the model.
- A
PARSE_FILE_ERRORmay occur when a user uploads a document containing special characters or non-standard formatting, possibly due to the file parser's insufficient compatibility with specific character encodings or document structures.
Verification of Configuration
- Test with SOP documents in the stem cell therapy domain that include multiple batches and different detection methods. Verify the model's ability to precisely distinguish and extract specific detection data for specific batches.
- Test with documents containing regulatory clauses and their interpretations. Check if the model's answers to compliance questions in multi-turn conversations can cite both clauses and explanations without contradictions.
- Upload a batch production record containing complex tables, charts, or special symbols. Confirm the system can parse it correctly and extract key field information.
- For common abbreviations and specialized terminology in documents, test the model's ability to accurately identify their meanings and use them correctly in context through multi-turn conversations.
The values provided are common starting points. Measure them 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.