Data Characteristics
Supplier audit data for clinical trial pre-screening originates from audit reports, qualification documents, Standard Operating Procedures (SOPs), quality management system documents, historical collaboration records, and regulatory compliance statements. These documents are typically in unstructured or semi-structured formats like PDF, Word, and Excel. Update frequencies vary; qualification documents may update annually, while audit reports generate after each audit. Document structures are complex, containing extensive specialized terminology, tables, and diagrams. Fields include audit findings, Corrective and Preventive Actions (CAPA), risk levels, and compliance status. Units are typically textual descriptions or numerical ranges. The data volume is large and distributed across multiple independent documents.
Constraints on Multi-Turn Conversations and Prompts
The unstructured nature of supplier audit data makes extracting precise information directly from raw documents for conversations challenging. Multi-turn conversations require handling complex contextual relationships. For example, a user might first inquire about a supplier's qualifications, then follow up on improvements for a specific risk point in their historical audits. Inconsistent document update frequencies necessitate knowledge base version management to ensure conversations rely on the latest, most accurate information. The presence of specialized terminology and tables demands more sophisticated prompt construction to guide the model in understanding and accurately parsing this specific content. Furthermore, numerous independent documents require the conversation system to integrate information across documents to answer complex, cross-domain questions.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 6000 Tokens | Ensures sufficient historical context retention in multi-turn conversations, covering common complex question-and-answer flows in supplier audits. |
temperature | 0.3 | Reduces randomness in model-generated content, ensuring professionalism and accuracy in answers, aligning with audit scenario rigor requirements. |
top_p | 0.7 | Limits the model's sampling range, further improving the stability and relevance of generated content, preventing deviation from the audit topic. |
Chunk size (Segment Length) | 800 characters | Balances document semantic integrity with model processing efficiency, reducing the risk of context loss after long document segmentation. |
Recall count (Recall Count) | Top 5 entries (Top 5) | Balances recall efficiency and relevance, ensuring coverage of core audit document segments while avoiding excessive noise. |
Rerank result count (Rerank Return Count) | Top 3 entries (Top 3) | Further optimizes ranking based on recall, prioritizing the most relevant information to directly support conversation generation. |
Common Mistakes
- Conversation results display in the sidebar instead of the main chat window. This typically occurs due to incorrect workflow configuration, where the AI conversation node's output connects to a component other than the main chat output.
- Each conversation requires a fresh start, unable to maintain context. This happens when the AI conversation node's
maxContextparameter is set too low or is incorrectly configured, preventing historical conversation information from being retained. - AI conversation responses consistently use Markdown syntax, even when not desired. This is because the prompt includes instructions forcing the model to use Markdown format, or the model's default behavior leans this way. Adjusting the prompt is necessary to explicitly limit the output format.
Verification Steps
- Conduct multi-turn simulated audit Q&A sessions. Verify the system's ability to accurately understand and maintain context, and assess its appropriate referencing of historical information.
- Randomly select multiple supplier audit documents. Ask questions regarding specific risk points and compliance requirements within them. Verify the system's ability to integrate information from different documents to provide correct answers.
- Test the system's parsing capabilities when faced with complex information such as specialized terminology and tabular data. Evaluate the accuracy of its references to this specific content in its answers.
- Check that the system's output format meets expectations, without superfluous Markdown markup, and that important information presents clearly and readably.
The values provided are common starting points and should be measured against specific 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.