Data Characteristics
Data for clinical trial pre-screening in regulatory affairs originates from major global clinical trial registries (e.g., ClinicalTrials.gov, European Medicines Agency EudraCT, Chinese Clinical Trial Registry CTR), public R&D pipeline reports from pharmaceutical companies, academic journal articles, and guidance documents and regulations from regulatory bodies. Update frequencies vary; clinical trial registration information may update daily, while regulatory documents typically update quarterly or annually. Document structures are diverse, including structured database records, unstructured PDF documents (e.g., study protocols, ethics approvals, investigator brochures), and semi-structured web content. Key fields include trial number, sponsor, investigational drug/device, indication, trial phase, primary/secondary endpoints, subject inclusion/exclusion criteria, trial sites, and research center contact information. Units involve dosage (mg, μg), time (days, weeks, months), and quantity (cases, units).
Constraints Imposed by These Characteristics on Multi-turn Conversation and Prompts
The heterogeneous nature of regulatory affairs data sources requires multi-turn dialogue systems to have robust content parsing capabilities for different information formats. For example, extracting subject inclusion/exclusion criteria from PDF documents requires accurately identifying and structuring text passages. Varying data update frequencies mean the system must regularly synchronize information from different sources to ensure multi-turn conversations are based on the latest data. If the system does not update promptly, conversations might reference outdated regulations or trial statuses. The specialized nature of fields and units requires prompt design to accurately understand and map user query terminology to corresponding fields in the knowledge base, preventing recall failures due to inconsistent terminology. In multi-turn conversations, users may ask in-depth questions about a specific drug's indications, development phase, or specific regulatory clauses. This requires the system to maintain context and perform precise knowledge retrieval and answering based on historical conversation content.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8 | The complexity of regulatory affairs requires retaining a longer conversation history to support multi-turn follow-up questions and context understanding. |
Chunk size (Segment Length) | 500 characters (characters) | Regulatory affairs documents often contain lengthy descriptive text. This length helps preserve semantic integrity and prevents truncation of key information. |
Recall count (Recall Count) | 7 entries (items) | Increasing the recall count helps cover more potentially relevant regulatory clauses or clinical trial information, improving hit rate. |
Similarity threshold (Similarity Threshold) | 0.75 | For precise matching of specialized domain terminology, a higher threshold filters out irrelevant general information, improving recall quality. |
Rerank result count (Reranked Return Count) | 3 entries (items) | Focusing on the most relevant few pieces of information reduces user reading burden and improves information retrieval efficiency. |
Max Prompt Tokens | 2048 token | Regulatory affairs questions are often rich in detail, requiring a longer prompt space to accommodate user questions, historical conversations, and recalled content. |
Three Common Mistakes
- In multi-turn conversations, the system fails to accurately connect a user's follow-up questions about different indications of a specific drug, leading to off-topic replies. This occurs because the context management mechanism does not effectively identify and store the conversation focus, treating different indications as independent questions.
- When a user asks about a specific regulatory clause, the system returns an outdated version of the regulation or prompts "no relevant information found." This happens because the knowledge base synchronization mechanism has delays, failing to update regulatory documents released by regulatory agencies in a timely manner, or the index does not cover all versions.
- When asking about trial design details, the system's reply does not include specific units in the subject enrollment criteria (e.g., "hemoglobin concentration below 10 g/dL"), only providing numerical values. This occurs because knowledge extraction or prompt generation fails to fully retain the complete unit information of fields in the original document.
How to Confirm Proper Configuration
- Select multiple typical regulatory affairs pre-screening scenarios and conduct multi-turn simulated conversations. Check if the system accurately understands and maintains conversation context and if the conversation flow meets expectations.
- Regularly compare the regulatory or clinical trial information retrieved by the system with the latest officially published versions. Ensure the accuracy and timeliness of knowledge base content, especially for key fields such as "trial status" and "regulation version number."
- Randomly select documents from the knowledge base that contain professional units. Conduct question-and-answer tests to verify whether the system correctly cites and displays all numerical values and units in its replies, such as drug dosages and time periods.
The values given 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.