Data Characteristics for This Category
Quality documents for regulatory submissions originate from various departments within pharmaceutical companies, such as R&D, clinical, manufacturing, and regulatory affairs. External sources include Contract Research Organizations (CROs) and Contract Manufacturing Organizations (CMOs). Data update frequency is relatively low, with revisions typically occurring at project milestones or when regulatory requirements change. Document types are diverse, including research reports, experimental records, batch production records, inspection reports, stability study reports, clinical trial reports, and non-clinical study reports. These often exist as PDFs, Word documents, or scanned images. Fields and units are highly specialized. Examples include content, purity, and dissolution rate in pharmaceutical research (units like %, mg/mL, min); subject ID, dosage, and adverse events in clinical trials (units like mg, times); and test methods and limits in quality standards (units like ppm, CFU/g). These documents are often complex, containing numerous figures, tables, and cross-references.
Constraints Imposed by These Characteristics on "Reference Tracing and Sourcing"
The low update frequency of regulatory submission documents demands robust version management during knowledge base construction. This ensures that cited sources always point to the currently approved or submitted version. The diversity and complex structure of documents, especially the abundance of figures and tables, challenge text extraction and semantic understanding. This can lead to critical information loss or misinterpretation during the RAG process. Highly specialized fields and units require the RAG system to accurately identify and preserve this information when citing, preventing ambiguity from unit conversion or missing context. For example, missing units for dosage information can lead to serious errors. Widespread internal cross-references in documents require the system to identify and link these internal references for comprehensive context. Additionally, scanned documents increase OCR difficulty, potentially introducing character errors that affect citation accuracy.
Configuration Settings
| Configuration Item | Suggested Value | Rationale for This Value |
|---|---|---|
Chunk Size | 500–800 characters | Regulatory submission documents often contain lengthy descriptions and complex logic. Chunks that are too short can split context, while chunks that are too long reduce retrieval precision. |
Overlap Size | 50–100 characters | Ensures semantic integrity at chunk boundaries, especially at the edges of tables or figure captions. |
Recall Count | 8–12 items | Regulatory submission questions often require multi-faceted information. Increasing the recall count improves coverage. |
Similarity Threshold | Calibrate based on actual measurements | Adjust this for specialized terminology and long sentence matching to avoid missed recalls due to low similarity of professional vocabulary. |
Rerank Count | 3–5 items | Focuses on core evidence most relevant to regulatory submission questions, reducing interference from irrelevant information. |
OCR Enabled | true | Regulatory submission documents contain many scanned images. Enabling OCR ensures comprehensive content retrievability. |
Three Common Mistakes
- The response contains links like
[Reference Source 1], but clicking them does not navigate to the specific location in the original document. This occurs because page numbers or anchor information for paragraphs were not correctly extracted and stored during document processing, leading to invalid traceability links. - The model's answer cites a numerical value from a batch production record, but the unit is incorrect or missing. This happens when document parsing fails to accurately identify and associate the value with its corresponding unit field.
- Asking about the incidence of an adverse event in a clinical trial results in an empty or irrelevant generic answer from the model. This is because the knowledge base failed to effectively identify and extract table data, leading to key statistical information not being indexed.
How to Confirm Correct Configuration
- Ask typical regulatory submission questions and verify that the cited source document links in the response accurately navigate to the corresponding page or section in the original text.
- Randomly select numerical citations from the model's response, check if their units match those in the source document, and verify the accuracy of the values.
- Test with documents containing tabular data to check if the model correctly understands and cites specific rows or columns from tables, such as drug dosages or subject counts.
- Verify that when a newer version of a document exists in the knowledge base, the model prioritizes citing information from the latest version and can trace its version number.
Note: 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.