Data Characteristics for This Category
SMO (Site Management Organization) regulations and SOP documents typically exist as PDFs, Word files, or internal knowledge management system pages. These documents cover GCP (Good Clinical Practice) guidelines, ethical review processes, project management details, investigator responsibilities, and data management SOPs. Documents are highly specialized, dense with terminology, and frequently cross-reference each other. Update frequency depends on regulatory changes, internal process optimizations, or new project initiations, generally quarterly or semi-annually. Some critical SOPs may be revised ad-hoc. Document length ranges from a few pages to several hundred, with a high degree of structure including tables of contents, chapter headings, and clause numbers.
Constraints Imposed by These Characteristics on Multiturn Conversations and Prompts
The dense specialized terminology and frequent cross-references in SMO regulatory documents require the multiturn conversation system to accurately identify professional terms and track context across documents when understanding user intent. The document update frequency necessitates regular synchronization of the knowledge base with the latest versions to avoid providing outdated information. Long, structured documents mean a single retrieval result may not cover the complete information for a user query, requiring multiturn conversations for information supplementation and clarification. Additionally, regulatory questions often involve compliance judgments, so the model must avoid generating vague or uncertain answers, ensuring accuracy and authority.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 500-800 characters | Balances context completeness with retrieval granularity, preventing long segments from diluting key information. |
Recall count (Number of Retrieved Chunks) | 5-8 chunks | Given the dense document cross-referencing, increasing retrieval volume helps capture related context. |
Similarity threshold (Similarity Threshold) | 0.7-0.8 | Ensures the professional relevance of retrieved content, avoiding generalized information. |
Rerank result count (Number of Reranked Chunks) | 3-5 chunks | In multiturn conversations, providing a small number of high-quality pieces of information facilitates user understanding and follow-up questions. |
maxContext | 3000-4000 tokens | Supports the context window for multiturn conversations, accommodating historical dialogue and retrieved content. |
SYSTEM_PROMPT | Calibrate based on actual testing | Guides the model to prioritize compliance and authority in its answers, avoiding speculative responses. |
Three Common Mistakes
- Model responses are truncated, but the status code shows 200. This may be because
maxContextormax_tokensparameters are set too low, causing the model to hit the limit before generating a complete answer. - In a multiturn conversation, the model fails to correctly understand a user's follow-up question based on previous dialogue. This may be because the
Similarity threshold(Similarity Threshold) is too high, preventing the retrieval of relevant information mentioned in historical dialogue, or theSYSTEM_PROMPTdoes not sufficiently emphasize context tracking. - A 500 error occurs when importing documents. This may be due to an unsupported file format, the file being too large and exceeding the
UPLOAD_FILE_MAX_SIZElimit, orPARSE_FILE_TIMEOUT_SECONDSbeing set too short, leading to a parsing timeout.
How to Confirm Proper Configuration
- Conduct multiturn conversation tests for typical SMO regulatory queries. Observe if the model can consistently and accurately answer related questions and handle contextual dependencies.
- In the conversation logs, check if the
Recall count(Number of Retrieved Chunks) for each retrieval matches the configuration and if the retrieved content is highly relevant. - Perform question-and-answer tests after simulating document updates to confirm the knowledge base update mechanism is effective and the model can provide answers based on the latest documents.
- Ask questions based on document sections containing specialized terminology and numerous internal references. Evaluate the model's ability to understand and summarize complex text.
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.