Reference and Traceability for Structured Analysis of R&D

Data Characteristics in this Category

Medical affairs data primarily comes from clinical trial reports, drug monographs, investigator brochures, medical literature reviews, regulatory documents, and internal medical communication materials. These documents are often in PDF, Word, or scanned image formats. They have complex structures, containing extensive specialized terminology, dosage units, clinical indicators, and statistical data. Update frequencies vary; regulatory documents and drug monographs may undergo annual revisions or temporary updates based on regulatory agency requirements. Clinical trial reports remain relatively stable after project completion, while medical literature is continuously emerging. Documents frequently include nested tables, figures, footnotes, and reference lists. Fields such as drug name, indication, adverse reactions, dosage, route of administration, and clinical endpoints are common. Units include mg, ml, μg/kg, %, and mmol/L. Different sources may express the same concept with subtle variations.

Constraints Imposed by These Characteristics on "Reference and Traceability"

The complex structure and specialized nature of medical affairs R&D documents demand high standards for reference and traceability. First, complex tables and figures within documents require that their data associations remain intact after structured parsing. This ensures accurate referencing to specific cells or chart areas in the original text. Second, specialized terminology and diverse unit systems require the model to precisely match context during recall and referencing, avoiding inaccurate traceability due to semantic ambiguity. For example, a reference to dosage must also trace back to the drug name and route of administration to form complete information. Third, variations in document sources and update frequencies necessitate that reference traceability can distinguish between the latest version of information and its original source, especially after revisions to regulatory documents or drug monographs, to ensure the current effective version is cited. Finally, embedded reference lists within documents require the system to identify and suggest original literature sources while referencing its own knowledge base content, enabling multi-level traceability.

Configuration Settings

Configuration Item	Recommended Value	Rationale for Recommendation
`Chunk size` (Segment Length)	`500–800 characters`	Medical documents have high information density per paragraph. Shorter segments hinder contextual understanding, while excessively long ones reduce recall precision.
`Recall count` (Number of Retrieved Items)	`8–12 items`	Ensures coverage of multi-faceted professional information, balancing recall quality with computational overhead.
`Similarity threshold` (Similarity Threshold)	`0.75–0.85`	Medical information demands high accuracy. A higher threshold filters for more relevant segments, reducing noise.
`Rerank result count` (Number of Reranked Items)	`5 items`	After reranking, the top few items have the highest quality and effectively support referencing.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Medical documents are typically large and structurally complex, requiring ample parsing time to avoid timeouts.
`maxContext`	`8000 Tokens`	Ensures the model has sufficient context to understand complex medical discussions and multi-dimensional data.

Three Common Mistakes

The cited paragraph in the output does not perfectly match the original content, or the pointed location in the original text is imprecise. This occurs when document parsing fails to adequately preserve the internal structure of complex elements like tables and figures, leading to overly coarse citation granularity.
The model fails to cite relevant content from the knowledge base, instead providing a generalized answer or stating "unable to find relevant information." This may be due to a similarity threshold set too high, filtering out slightly less relevant but valid segments, or an insufficient number of retrieved items to cover comprehensive information.
When processing frequently updated documents (e.g., regulatory files), outdated information is cited. This happens when the knowledge base update mechanism is not synchronized with the document source's update frequency, or a document version management strategy is missing, leading the system to retrieve expired data.

How to Confirm Proper Configuration

Randomly select 10 queries involving complex tables and figures. Check if the cited sources precisely point to specific cells or chart areas in the original text and verify the accuracy of the cited content.
For queries containing specialized terminology and multi-unit information, verify if the cited paragraphs in the model's output fully reflect the contextual semantics of the terminology and unit information. Check if the similarity threshold is within a reasonable range.
Select recently updated regulatory documents or drug monographs and perform relevant queries. Confirm that the cited sources point to the latest version of the document and check the consistency between the knowledge base update time and the document source update time.
For cases where the model fails to cite knowledge base content, examine the number of retrieved items and similarity score in the backend logs to assess if it was caused by insufficient recall or an excessively high threshold.

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.