Data Characteristics
mRNA vaccine clinical trial data originates from global clinical trial registries (e.g., ClinicalTrials.gov, WHO ICTRP) and internal pharmaceutical company R&D documents. This data updates frequently with new trial registrations, progress updates, and results. Public data often comes in structured or semi-structured formats, including trial protocols, subject inclusion/exclusion criteria, dosage information, adverse event reports, and biomarker data. Unstructured data exists in investigator brochures, meeting minutes, and internal reports. Fields and units are highly specialized, for example, "subject age" in "years," "weight" in "kilograms," and "specific biomarker expression" in "ng/mL" or "copies/mL." These often include specific medical terminology and coding systems (e.g., ICD-10, MedDRA).
Constraints Imposed by These Characteristics on Multiturn Conversation and Prompts
High-frequency data updates require FastGPT to retrieve the latest information during multiturn conversations, avoiding references to outdated trial statuses. Specialized fields, units, and medical terminology demand high precision in prompts. The AI must understand and correctly interpret this information to prevent ambiguity. For example, prompts must clearly distinguish between "age range" and "median age." The mix of structured and unstructured data means the AI needs to extract key information from various sources and maintain context consistency across multiple interactions. For instance, when a user asks about "enrollment status for patients with a specific genetic mutation," the AI must combine structured inclusion criteria and unstructured genetic test reports. Additionally, complex decision trees in clinical trials, such as determining subject eligibility based on multiple conditions, require prompt designs that guide the AI in logical reasoning.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 2000 characters | Clinical trial protocols are lengthy; a longer context helps maintain complete context and prevents loss of critical information. |
temperature | 0.3 | Clinical trial prescreening requires rigorous and factually accurate answers; a low temperature reduces model creativity and increases certainty. |
topP | 0.5 | Further limits the diversity of model generation, focusing on high-probability words to ensure output content closely matches medical terminology. |
prompt | Contains keywords like "mRNA vaccine clinical trials, subject inclusion/exclusion criteria, biomarkers, adverse events" | Clearly defines the AI's focus area, guiding the model to concentrate on core elements of clinical trial prescreening and improving relevance. |
maxTokens | 500 | Ensures the AI can provide sufficiently detailed explanations or judgments in a single turn, meeting user needs for complex information queries. |
embeddingModel | text-embedding-ada-002 | Suitable for embedding specialized terminology in the biomedical field, improving the accuracy of vector retrieval. |
Common Pitfalls
- The completion reason field output by the AI conversation component is empty when referenced in subsequent components. This occurs if the preceding AI conversation node does not correctly configure the
outputfield, or if the field name does not match the reference in the subsequent component, preventing data transfer. - In multiturn conversations, the AI fails to correctly recognize spaces in prompts, leading to failed medical term matching. This happens when special characters or formats in prompts, such as consecutive spaces or tabs, are not effectively handled during model training or tokenization preprocessing.
- The AI repeatedly performs ineffective regeneration and cannot break out of a loop. This occurs when the problem classification logic is too broad or flawed, failing to accurately identify "correct" text, causing the AI to fall into an infinite iteration.
Verification Steps
- Conduct simulated conversations with multiple test cases containing complex medical terminology and inclusion criteria. Check if the AI's responses accurately cite trial data without errors.
- In the FastGPT interface, review conversation details to confirm that the
maxContextsetting matches the number of context items actually referenced. - Design test cases with negative conditions and multiple judgments. Verify if the AI can correctly perform logical reasoning and provide expected prescreening results.
Note: The values provided are common starting points. Measure them against your 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.