Multiturn Conversation and Prompts for Neurodegenerative

Data Characteristics

Clinical trial data in the neurodegenerative disease domain originates from national or international clinical trial registries (e.g., ClinicalTrials.gov, WHO ICTRP), academic journals, conference abstracts, and pharmaceutical company public reports. Data update frequencies vary; registry data typically updates when trial statuses change, while journal data depends on publication cycles. Document structures are complex, including protocol summaries, eligibility criteria, study designs, interventions, primary/secondary endpoints, and participant characteristics. Fields are rich, such as NCT ID, Condition, Intervention, Eligibility Criteria, and Study Status. Units are diverse; for example, age is typically in years, MMSE score in points, and dosage may be in mg or μg.

Constraints Imposed by These Characteristics on Multiturn Conversation and Prompts

The high specialization and complexity of neurodegenerative disease clinical trial data impose specific requirements on multiturn conversation and prompt design. First, medical terminology and numerical ranges within eligibility criteria require precise identification and parsing, such as expressions like "MMSE score between 20-26 points." Second, individual variability in disease progression often leads to trial designs with multiple subgroups or stratifications. Multiturn conversations must guide users to progressively clarify their specific conditions of interest. Irregular data updates mean prompts must emphasize information timeliness and guide users to confirm data source update dates. Additionally, some fields (e.g., disease severity, genotype) may have multiple expressions or encoding methods. Prompts need to be fault-tolerant and guide users to correct or supplement information. The long document structure challenges RAG retrieval accuracy and completeness, requiring more refined chunking strategies and context management.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`maxContext`	`2000 tokens`	Neurodegenerative disease descriptions often contain complex pathophysiological information, requiring a longer context for coherence.
`Chunk size` (Chunk Length)	`500 characters` (characters)	Individual eligibility criteria or study objectives in clinical trial protocols typically fall within this length, facilitating precise retrieval.
`Recall count` (Retrieval Count)	`Top 8 entries` (top 8)	Considering disease heterogeneity and trial design complexity, increasing the retrieval count covers more potentially relevant information.
`Similarity threshold` (Similarity Threshold)	`0.78`	Ensures retrieved clinical trial information is highly relevant to the user query, avoiding the introduction of irrelevant complex medical concepts.
`Rerank result count` (Reranked Return Count)	`Top 3 entries` (top 3)	Based on a high retrieval count, reranking selects the three most relevant items, improving the quality of the final presentation.
`system_prompt`	Clearly define the role as "Clinical Trial Pre-screening Assistant," emphasizing data sources and timeliness constraints.	Ensures the AI provides accurate, timely information within a specialized domain and guides users to focus on key attributes.

Three Common Mistakes

The conversation states "no relevant trials found." This may occur if the user's disease description is too broad or uses non-standard medical terminology, leading to RAG retrieval failure or excessively low similarity.
The AI misinterprets numerical ranges or ignores units when processing eligibility criteria. This manifests as recommended trials not matching the user's actual conditions, typically because the prompt did not sufficiently emphasize the rigor of numerical parsing.
After a user uploads a medical report in PDF format, the AI fails to extract key information, reporting file parsing failed or field is empty. This may be due to the file parser's inability to handle complex tables or scanned documents, or the lack of OCR pre-processing configured for medical documents.

How to Confirm Proper Configuration

Select 5-10 test cases with typical eligibility criteria (e.g., age range, MMSE score). Observe whether the AI accurately identifies and matches corresponding clinical trials.
Simulate user questions about data update frequency and sources. Check if the AI's response explicitly mentions authoritative registries like ClinicalTrials.gov and provides the latest update dates.
Upload a clinical trial protocol PDF containing complex tables and medical terminology. Verify that the system successfully parses the file content and extracts core fields such as NCT ID and Intervention.

The values provided are common starting points and should be measured 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.