Data Characteristics
Molecular diagnostics regulations and SOP documents primarily come from standards published by the National Medical Products Administration (NMPA) and the International Organization for Standardization (ISO), as well as internal quality management system documents from medical device manufacturers. These documents are typically in PDF, Word, or XML format. Update frequency is relatively low, usually occurring every few months to several years, coinciding with policy adjustments or technical standard updates. Document structures are rigorous, containing numerous chapters, clause numbers, and definitions. Content covers detailed aspects such as reagent batch management, instrument calibration, operating procedures, quality control, result interpretation, and adverse event reporting. Fields often include batch number, expiration date, calibrator values, quality control ranges, detection limits, and linear ranges. Units include IU/mL, copies/mL, ng/μL, ℃, and min, requiring high precision.
Constraints Imposed by These Characteristics on Multi-turn Conversations and Prompts
The rigorous nature and low update frequency of molecular diagnostics regulations demand extremely high stability and authority from knowledge base recall. In multi-turn conversations, users often need to ask follow-up questions about specific clauses or operating steps. This requires the system to precisely locate and provide unambiguous explanations. The large number of specialized terms and measurement units in the documents means prompt design must fully consider contextual semantics to avoid recall deviations due to synonyms or near-synonyms. For example, inquiries about "batch" might refer to production batches, calibrator batches, or reagent batches; the system needs to distinguish these based on conversation history. Additionally, common numbering systems in documents (e.g., YY/T standard numbers, ISO standard numbers) must serve as important retrieval identifiers, ensuring prompts can effectively utilize this structured information.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 500-800 characters | Ensures each segment contains a complete clause or operating step, preventing semantic fragmentation. |
Recall count (Recall Count) | Top 8 | Given the rigorous nature of molecular diagnostics regulations, provides sufficient relevant context for the model. |
Similarity threshold (Similarity Threshold) | 0.82-0.88 | Improves recall precision, reduces interference from irrelevant or ambiguous clauses, and ensures answer authority. |
Rerank result count (Rerank Return Count) | Top 3 | Further improves the ranking of the most relevant content based on a high recall count. |
maxContext | 3000-4000 token | Accommodates multi-turn conversation history and recalled content, ensuring contextual coherence for complex questions. |
TEMPERATURE | 0.1-0.3 | Reduces the randomness of model-generated content, ensuring answer accuracy and consistency. |
Three Common Pitfalls
- When executing long text tasks, the debugging page shows the task completed but reports "The value of "offset" is out of range.": This occurs because during file preprocessing or chunking, a segment exceeds the system's maximum allowed character length, leading to vectorization failure.
- In multi-turn conversations, the system cannot distinguish between instrument calibration and reagent calibration when asked about "calibration": This happens because the prompt template does not adequately guide the model to identify specific entity types in the context, or the relevant documents in the knowledge base do not effectively differentiate these concepts during segmentation.
- When a user asks "How to perform batch release," the recall results include many irrelevant production management documents: This indicates that the knowledge base index or embedding model's semantic understanding of "batch release" is not precise enough, failing to effectively distinguish different "batch" contexts.
How to Confirm Correct Configuration
- Design multi-turn question-and-answer paths for core regulatory clauses and SOP steps. Verify the system can consistently provide accurate and coherent answers after follow-up questions.
- Select complex questions containing numerous specialized terms and units. Check if recall results accurately point to relevant definitions and value ranges, and verify the consistency of the model's output with the original text.
- Simulate user inquiries about specific batch numbers, expiration dates, or standard numbers. Observe if the system can effectively use this structured information for retrieval and answering, and verify if the answer includes corresponding citation information.
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.