Data Characteristics
IVD (In Vitro Diagnostic) reagent quality documentation includes registration certificates, instructions for use (IFU), batch inspection reports, SOPs (Standard Operating Procedures), methodology validation reports, and stability study reports. These documents are typically stored in PDF, Word, or Excel formats. Data update frequency is relatively low, occurring mainly during product registration changes, batch releases, or quality system revisions. Document structures are highly standardized. For example, IFUs contain fixed fields such as [Product Name], [Intended Use], [Principle of the Test], [Main Components], [Storage Conditions and Shelf Life], and [Sample Requirements]. Batch inspection reports detail information like [Batch Number], [Production Date], [Test Item], [Test Result], [Acceptance Criteria], and [Inspector]. Units involved include mol/L, ng/mL, U/L, ℃, and %, with extremely high demands for precision and consistency.
Constraints from Data Characteristics on Multi-Turn Conversations and Prompts
The standardized structure and low update frequency of IVD reagent quality documentation lead to high accuracy and strong timeliness for knowledge bases built upon them. In multi-turn conversations, users often need to query specific parameters for a particular product batch or a specific test method. This requires the model to accurately understand entities like [Product Name], [Batch Number], and [Test Item] within the user's intent, and to extract precise values or steps from the corresponding documents. Due to the high precision requirements, the model must avoid generalization and vague descriptions when generating responses. Instead, it must cite original statements or data from the documents. The low update frequency means that knowledge base construction can focus on in-depth analysis, reducing investment in real-time update mechanisms. However, it is crucial to ensure the traceability of historical document versions to handle queries regarding differences between various production batches or registration versions.
Configuration Settings
| Parameter | Recommended Value | Rationale |
|---|---|---|
Chunk Size | 300–500 characters | Key information in IVD documents is typically concentrated in short paragraphs. Overly long chunks can introduce irrelevant context, reducing retrieval accuracy. |
Recall Count | Top 5 | Given the specialized and precise nature of the documents, recalling a small number of highly relevant paragraphs helps the model focus on key information and reduces irrelevant interference. |
Similarity Threshold | 0.78–0.85 | Ensures that recalled document snippets are highly relevant to the user query, filtering out content with low semantic similarity to avoid misleading information. |
Rerank Count | Top 3 | Further refines the retrieval results, presenting the top 3 most relevant items to the large language model, improving the accuracy and focus of the response. |
maxContext | 4096 tokens | Ensures the model can fully receive and understand critical recalled information, especially when processing SOPs or methodology validation reports, which require longer contexts. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing IVD documents (e.g., large PDF reports) can be time-consuming. Increasing the timeout prevents parsing interruptions. |
Common Pitfalls
- The conversation result states "Unable to answer based on existing information" or provides a generalized answer. This happens when the system fails to accurately identify entities like [Batch Number] or [Test Item] in the user's query, leading to an overly broad or narrow document recall.
- The model cites incorrect values or units in its answer. This occurs during the knowledge base construction phase due to inaccurate parsing of [Test Result] and [Acceptance Criteria] fields in Excel-formatted batch inspection reports, leading to data extraction errors.
- When a user asks about the content of a specific historical document version, the model provides information from the latest version. This is because document management and version control lack clear metadata tags, preventing differentiation between different document versions during retrieval.
Verification of Configuration
- For the same product with different batches and registration versions, ask multi-turn questions about specific parameters or SOP steps. Verify that the model's answers exactly match the corresponding original documents.
- Randomly select a batch of queries containing key information such as [Product Name], [Intended Use], and [Principle of the Test]. Check if the model can accurately extract and organize this information into structured answers from the IFU.
- Simulate user queries for a [Test Result] or [Acceptance Criteria] for a specific test item. Verify if the model can locate the correct values and units from the batch inspection report and explain their meaning.
Note: The values provided are common starting points. They 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.