Data Characteristics
Quality document management in biopharmaceuticals, specifically for clinical trial pre-screening, involves documents such as Standard Operating Procedures (SOPs), batch production records, inspection reports, deviation reports, and change control documents. These documents are typically in PDF format, have a standardized internal structure, and contain significant structured and semi-structured information. Data update frequency is relatively low, occurring primarily during procedure revisions, completion of batch production, or generation of new reports. Documents contain highly specific fields, including batch numbers, production dates, expiration dates, inspection items, results, units (e.g., mg/mL, pH value, IU), and key personnel signatures.
Constraints from these Characteristics on Multiturn Conversation and Prompts
The standardized and specialized nature of quality documents requires the multiturn conversation system to accurately recognize technical terms and abbreviations when understanding user intent. The low document update frequency means knowledge base construction can focus on in-depth analysis. Each update must ensure index completeness and timeliness. The precise numerical values and units in the documents require prompt design to guide the model to focus on these details, avoiding vague answers. Furthermore, document content often involves regulatory compliance, so the conversation system needs traceability. This means the system must indicate the specific document and paragraph source for its answers, which demands higher precision in the retrieval and generation phases.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Size) | 500–800 characters | Quality document paragraphs have strong logical integrity. Chunks that are too short might cut off critical information, while chunks that are too long increase noise. |
Recall count (Recall Count) | Top 5–8 chunks | This ensures coverage of multiple relevant document segments, increasing recall comprehensiveness while controlling the model's input length. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | This ensures retrieved results are highly relevant to the query, reducing interference from irrelevant document segments. |
Rerank result count (Reranked Count) | Top 3 chunks | This further refines search results, providing the most relevant and precise information to the large language model. |
maxContext | 3000–4000 tokens | This balances the amount of retrieved contextual information with the large language model's processing capability, preventing truncation of critical information. |
prompt instruction length | 150–250 characters | This guides the large language model to focus on key fields in the document, such as batch numbers, dates, and units, in its answers. |
Common Mistakes
- The knowledge base retrieval result is empty during a conversation, but content is retrieved during knowledge base testing. This typically occurs because the conversation context does not match the retrieval terms, or the knowledge base index is not fully synchronized.
- The conversation returns a
404 Invalid URL (POST /api/chat/completions)error. This usually indicates an incorrect backend service address configuration or a network issue causing the model API call to fail. - The workflow ends prematurely halfway through execution, with the AI having already replied but subsequent processes not executing. This might be due to a length restriction in the prompt (e.g.,
300 characters) causing the model to end generation early, and the workflow not receiving the expected end signal.
How to Verify Configuration
- Design test cases for different types of quality documents (SOPs, inspection reports) to verify the system's ability to accurately extract key information, such as batch numbers and expiration dates.
- Simulate multi-turn questioning by a user. Observe if the system can still retrieve correct information from relevant documents after context switching and if it can indicate the source document ID.
- Check if the model's output includes explicit numerical values and units from the documents. Compare these with the original documents to ensure numerical accuracy.
- Verify that the system correctly understands and provides relevant explanations or citations when handling technical terms and abbreviations (e.g.,
GMP,ICH).
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.