Data Characteristics in this Category
Medical affairs regulatory submission preparation involves data primarily from clinical trial reports, investigator brochures, published medical literature, drug labels, regulatory guidelines, and internal Standard Operating Procedures (SOPs). Data update frequencies vary. Clinical trial data typically generate and update incrementally throughout a project cycle. Regulatory guidelines may revise periodically. Document structures often include PDF reports, Word format protocols or guidelines, and tables within structured databases. Fields and units are highly specialized, such as dosage units (mg/kg), time points (hours, days, weeks), and statistical indicators (P-values, confidence intervals). Abbreviations and industry-specific terminology are common.
Constraints from these Characteristics on "Source Citation and Traceability"
The specialized and diverse nature of medical affairs data imposes specific requirements on source citation and traceability. First, extensive specialized terminology and abbreviations in documents require the knowledge base to accurately identify context during segmentation. This prevents semantic loss from improper splitting, which would affect citation accuracy. Second, complex tables and charts in PDF reports and scanned documents require advanced parsing capabilities to ensure effective indexing and citation of tabular data. Third, updates to regulatory documents and guidelines mean the knowledge base must support version management and incremental updates to ensure the recency and compliance of cited content. Finally, strict requirements for measurement units and statistical indicators necessitate precise citation output to the original text location for manual verification.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Chunk size | 800–1200 characters | Medical literature paragraphs have high information density. Longer segments retain more context and improve recognition of specialized terminology. |
Recall count | Top 5 entries | This ensures coverage of core information points while avoiding excessive irrelevant snippets, balancing recall and precision. |
Similarity threshold | 0.78–0.85 | Domain terminology has high similarity. Increasing the threshold precisely matches relevant paragraphs and reduces incorrect citations. |
Rerank result count | Top 3 entries | This further optimizes recall results, placing the most relevant few snippets at the top for accurate model citation. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing large clinical study reports or complex PDF documents requires longer parsing times to avoid timeouts. |
maxContext | 32000 | This handles complex queries and multi-document comprehensive analysis, providing sufficient context window for deep reasoning and citation. |
Common Mistakes
- AI dialogue output shows garbled citation numbers, which then revert to quotation marks. This occurs due to improper front-end rendering or character encoding, failing to correctly parse citation markers returned by the backend.
- Tool invocation modules in workflows cannot cite after connecting to the knowledge base. This typically happens because the tool module's output format is incompatible with the knowledge base's citation mechanism, or the tool's results are not correctly passed to the knowledge base citation processing stage.
- The AI still provides knowledge base file citations for questions not present in the knowledge base. This may occur if the
Similarity threshold(similarity threshold) is set too low, leading to matching low-similarity knowledge base snippets even for irrelevant queries.
How to Confirm Correct Configuration
- Select a typical clinical study report. Input a query for one of its key results. Verify that the output citation points to the exact location of that result in the report.
- Upload a regulatory guidance document containing complex tables. Query a specific data point within a table. Check if the citation correctly traces back to that table.
- Ask a medical question that is explicitly not present in the knowledge base. Confirm that the AI output does not include knowledge base citations, or that it cites a "no relevant information found" message.
- After a knowledge base update, query key information that was modified in the old version. Verify that the AI cites content from the latest version.
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.