Data Characteristics in This Category
Data for clinical trial pre-screening in academic promotion primarily originates from public clinical trial registries (e.g., ClinicalTrials.gov, WHO ICTRP), internal pharmaceutical company trial data, academic journal papers, conference abstracts, and regulatory agency guidelines and reports. This data updates frequently. Registries, in particular, may see new trials registered or existing trial statuses updated weekly or even daily. Document structures typically include structured trial protocols (e.g., study objectives, inclusion/exclusion criteria, interventions, primary/secondary endpoints, study sites, study status, PI information), unstructured research paper abstracts and full texts, and semi-structured regulatory documents. Fields and units are highly specialized. For example, "primary endpoint" might involve "overall survival (OS)" in "months," "progression-free survival (PFS)" in "weeks," or "adverse event rate" as a "percentage."
Constraints Imposed by These Characteristics on Multi-turn Conversation and Prompts
High-frequency data updates demand real-time accuracy in multi-turn conversations. Prompt design must guide the model to prioritize retrieval of the latest data. The coexistence of structured and unstructured data requires hybrid retrieval capabilities from the knowledge base. Prompts must explicitly define the retrieval scope, for example, "query the latest published Phase III clinical trials for [disease name]." Specialized fields and units necessitate high precision from the model in understanding and generating queries. Prompts should include definitions or context for key medical terms to avoid ambiguous matches. For instance, if a user asks "What about OS?", the model must identify "OS" as overall survival and provide specific values and units based on the knowledge base. Furthermore, dynamic changes in trial status (ongoing, completed, recruiting) require multi-turn conversations to track and update user-focused trial statuses. Prompt design must consider status filtering conditions.
Configuration Settings
| Configuration Item | Suggested Value | Rationale for this Value |
|---|---|---|
maxContext | 8 | Ensures the model can effectively recall key information from previous turns in multi-turn conversations, maintaining dialogue coherence. |
Chunk size | 500–800 characters | Balances semantic completeness of text and retrieval efficiency, preventing excessively long texts from diluting key information. |
Recall count | 10–15 entries | Clinical trial information is complex; increasing recall count raises the probability of selecting highly relevant documents. |
Similarity threshold | 0.75–0.85 | Ensures the professionalism and accuracy of recalled content, filtering out low-relevance general medical texts. |
Rerank result count | 5 entries | Based on a high recall volume, re-ranking selects the most relevant core trial information to present to the user. |
SEARCH_TIMEOUT_SECONDS | 30 seconds | Addresses the retrieval pressure of complex clinical trial data, allowing sufficient time for efficient retrieval. |
Three Common Pitfalls
- When the chat application calls the API, the returned results do not match platform testing. This may be due to differences in API call parameters compared to the default configurations (e.g.,
knowledge_base_idormodel_config) in the platform's test environment. - The bot fails to accurately understand specialized terms or abbreviations. This occurs when the knowledge base lacks definitions or contextual associations for corresponding terms, or the prompt does not explicitly instruct the model to parse terms.
- The user's questions cannot retrieve the latest trial data. This may be due to insufficient knowledge base update frequency, or the retrieval mechanism failing to prioritize documents with the latest timestamps.
How to Confirm Correct Configuration
- Test with a series of questions containing the latest clinical trial information. Check if the answers cite the latest trial registration numbers or update dates.
- Construct questions containing specific medical abbreviations and specialized terms. Observe if the model can correctly interpret them and provide relevant information based on the knowledge base.
- Simulate multi-turn conversations. In different turns, follow up on trial details (e.g., inclusion/exclusion criteria, primary endpoint data). Evaluate if the model can maintain dialogue coherence and provide accurate answers.
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.