Multiturn Conversation and Prompts for IVD Diagnostic

Data Characteristics for this Category

IVD diagnostic reagent registration documents draw from various sources. These include product manuals, registration certificates, clinical evaluation reports, quality management system files, production process documents, stability study reports, and risk analysis reports. Documents are typically in PDF, Word, or Excel formats. Data updates are irregular, driven by regulatory changes, product iterations, and clinical trial results. Document structures are highly standardized, adhering to templates from the National Medical Products Administration (NMPA) or international standards (e.g., IVDR). They include clear section and sub-section headings. Fields and units are highly specialized, such as "Limit of Detection (LOD)," "Linear Range," "Intra-batch and Inter-batch Precision (CV%)," "Specificity (%)," "Sensitivity (%)," and "Storage Conditions (2-8°C)." These field values often include specific units and method descriptions.

Constraints Imposed by These Characteristics on Multiturn Conversation and Prompts

The standardized document structure of IVD registration materials allows precise information retrieval using specific prompts. For example, a query can directly target the "Detection Principle" section within a "Product Manual." Highly specialized fields and units demand precise prompt design to avoid generalized questions that lead to information bias. For questions involving numerical comparisons or range assessments, multiturn conversation is crucial. This allows for gradual refinement of query conditions, such as first asking for "a reagent's linear range" and then following up with "does this linear range meet specific standards?" Irregular data updates require the system to incrementally update the knowledge base regularly, ensuring real-time accuracy of conversation results. Furthermore, information extraction varies across document types (e.g., PDF and Excel). Prompts must adapt to multiple data structures, such as extracting specific metric values from tables.

Configuration Settings

Configuration Item	Recommended Value	Rationale for this Value
`maxContext`	`8`	Ensures multiturn conversations cover common IVD registration document query scenarios while controlling computational resource consumption.
`Chunk size` (Chunk Size)	`500–800 characters`	IVD document paragraphs are of moderate length. This range helps maintain semantic completeness and improves retrieval accuracy.
`Recall count` (Recall Count)	`Top 5–8 items`	Relevant information density is high in registration documents. Increasing the recall count appropriately improves relevance coverage.
`Similarity threshold` (Similarity Threshold)	`0.75`	IVD professional terminology requires high precision. A high threshold filters out low-relevance recall results.
`Rerank result count` (Rerank Return Count)	`3`	After reranking, returning a small number of the most relevant items enhances the conciseness and accuracy of the final answer.
`max_tokens`	`1024`	Ensures the model can output complete technical specifications, regulatory clauses, or analytical conclusions, preventing truncation.

Three Common Mistakes

Conversation responses are exceptionally brief, failing to provide complete information. This usually occurs when max_tokens is set too low, truncating the model's output before completion.
When a user asks for a specific metric, the system returns a general description. This might be due to a similarity threshold set too low, recalling many non-core relevant paragraphs and diluting key information.
In multiturn conversations, the system inadequately understands the context from previous turns, leading to disjointed subsequent answers. This often happens when maxContext is insufficient, failing to retain historical conversation information effectively.

How to Confirm Proper Configuration

Conduct multiturn conversation tests for typical IVD registration document query scenarios. Check the completeness, accuracy, and coherence of responses.
Use test questions containing specific professional terms and numerical values. Verify if the system can accurately extract and present this information, for example, by querying "a reagent's inter-batch variation (CV%)."
Simulate user needs for interpreting regulatory clauses or technical standards. Evaluate if the system can provide compliance judgments or explanations based on the knowledge base content. Check if the cited document source is correct.
Test the system's ability to extract information from different document formats (e.g., charts in PDFs, data tables in Excel). Ensure key fields and units are correctly identified.

The values provided are common starting points and should be measured 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.