Data Characteristics
siRNA nucleic acid drug clinical trial data originates primarily from clinical trial registries (such as ClinicalTrials.gov and the European Medicines Agency EudraCT database) and internal drug development organization documents. This data updates frequently, with continuous registration of new trials, progress updates, and result publications. Document structures typically include trial protocols, investigator brochures, subject informed consent forms, ethics approvals, data management plans, statistical analysis plans, and final clinical study reports. Fields cover subject inclusion criteria, exclusion criteria, dosage, administration route, adverse events, efficacy indicators, and biomarker data. Data formats vary, including structured tables, unstructured text, PDF documents, and XML files. Specific units like nM (nanomolar) and mg/kg (milligrams per kilogram) are commonly used to describe drug concentration and dosage.
Constraints from Data Characteristics on Multiturn Conversation and Prompting
The diversity and high update frequency of siRNA nucleic acid drug clinical trial data require efficient information extraction and integration capabilities from a multiturn conversation system. The presence of unstructured documents means that keyword-based retrieval alone is ineffective, necessitating more advanced semantic understanding. Multiturn conversations need to accurately identify dosage ranges and biomarker thresholds within subject inclusion and exclusion criteria. These are often presented with specific units and numerical values, requiring the model to understand the relationship between numbers and units. Due to rapid data updates, prompt design must consider timeliness to avoid citing outdated information. Additionally, users may mention multiple efficacy indicators or adverse events during a conversation. The system needs to maintain contextual coherence under complex queries and dynamically adjust information retrieval strategies based on user intent.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8192 | Accommodates long documents and complex multiturn conversations, ensuring contextual coherence. |
Chunk size (Chunk Size) | 500 characters (characters) | Balances retrieval granularity with semantic completeness, avoiding the splitting of critical information. |
Recall count (Recall Count) | Top 10 entries (top 10) | Increases the likelihood of recalling relevant information, covering more potential matches. |
Similarity threshold (Similarity Threshold) | 0.75 | Filters out low-relevance results, reduces noise, and focuses on core information. |
Rerank result count (Reranked Return Count) | Top 5 entries (top 5) | Improves the relevance and ranking quality of the final presented results. |
promptTemplate | Calibrate by actual measurement | Needs to include explicit extraction instructions for key information such as dosage, units, and biomarkers. |
Common Pitfalls
- AI conversation stalls with no response. This typically results from backend service connection timeouts or model API call failures. Check network configuration and API keys.
- Inability to display LaTeX formatted formulas in conversations. This usually occurs when the frontend rendering component is not correctly configured or compatible, or when the display capabilities of the production environment differ from the debugging environment.
- Intermediate AI conversation results from a workflow are outputted in the final result. This happens when the workflow design does not explicitly specify the visibility of intermediate step outputs or does not filter results.
Verification Steps
- In a test environment, conduct multiturn conversations using complex queries involving dosage, administration routes, and biomarkers. Verify that the system accurately extracts and integrates information.
- Submit queries containing LaTeX formatted formulas. Confirm that the deployed conversation interface correctly renders the formulas.
- Simulate clinical trial inclusion and exclusion criteria screening scenarios. Verify that the system provides logically consistent screening results after multiturn interaction.
- Examine the output of AI conversation steps within the workflow. Ensure that unintended intermediate results do not appear in the final user interface.
The values provided are common starting points. Measure performance 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.