Data Characteristics
Contract Research Organization (CRO) regulation and Standard Operating Procedure (SOP) data primarily originate from internal regulatory documents, project operation manuals, quality management system files, and training materials. These documents are typically stored in formats such as PDF, Word, and Excel. Their content covers detailed operational procedures, responsibilities, and risk control measures across various stages, from preclinical research and clinical trials to data management and biostatistics. Documents are frequently updated, especially with regulatory changes, new project initiations, or internal process optimizations. Document structures are often hierarchical, including standard sections like introduction, purpose, scope, responsibilities, procedures, and appendices. They extensively use specialized terminology, abbreviations, and internal references. Key metrics or parameters may appear in tables, involving dosage, time points, and sample sizes, with units clearly specified.
Constraints on Multiturn Conversation and Prompts
The complexity and specialized nature of CRO regulatory documents require a multiturn conversation system to possess robust semantic understanding and context maintenance capabilities. Frequent specialized terminology and abbreviations in documents necessitate prompt design that considers glossaries or alias mapping to accurately identify user intent. The frequent updates to documents mean the knowledge base needs an efficient synchronization mechanism to avoid providing outdated information. The hierarchical document structure requires the system, in multiturn conversations, to delve into specific sections or paragraphs for information extraction based on user follow-up questions, rather than staying at a superficial level. Furthermore, queries involving specific parameters or processes require the system to precisely extract numerical information from tables or structured text and handle unit conversions or comparisons, demanding higher data extraction requirements in prompts.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8 | Addresses common multiturn follow-ups and contextual dependencies in CRO regulation queries, balancing performance and accuracy. |
Chunk size | 800–1200 characters | Most CRO regulation document paragraphs have moderate length; this range helps maintain semantic integrity and reduces segmentation loss. |
Recall count | Top 5 entries | Ensures coverage of multiple relevant regulatory clauses for complex queries, improving recall rate. |
Similarity threshold | 0.75–0.85 | For texts with many specialized terms, appropriately increasing the threshold can reduce irrelevant recalls and ensure precision. |
Rerank result count | 3 | In multiturn conversations, re-ranking the 3 most relevant results enhances user experience and answer quality. |
promptTemplate | Calibrate based on actual testing | Must include clear "role setting" and "task requirements" to guide the model to focus on regulation interpretation and process description. |
Common Pitfalls
- When calling a workflow, the conversation log is empty or shows failure. This indicates the workflow node did not return the expected result. The model's return format might not conform to workflow expectations, or an internal workflow logic error occurred but was not correctly reflected in the interface.
- In an application conversation, querying a specific regulatory clause returns an outdated or deprecated version. This is due to an imperfect knowledge base synchronization mechanism, or document version management is not effectively linked to the knowledge base, leading to the retrieval of obsolete data.
- When asking about specific operational procedures, the system fails to provide complete steps or the steps have logical discontinuities. The returned results are incomplete. This may be because document segmentation is too granular, preventing individual segments from providing complete context, or the prompt did not explicitly instruct the model to integrate steps.
Verification Methods
- Select 10 typical and complex CRO regulation query scenarios. Simulate user multiturn conversations and verify if the system's answers are accurate, complete, and compliant with the latest regulations.
- For queries involving tabular data and specialized terminology, verify if the system can accurately extract numerical values and explain terms. Check if extracted key fields and units are correct.
- After a knowledge base update, immediately test queries related to the updated content. Confirm the system responds promptly with the latest information and verify if queries for old version information are correctly guided or prompted as outdated.
Note: The values provided are common starting points. They 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.