Multiturn Conversation and Prompts for Neurodegenerative

Data Characteristics for This Category

Data for neurodegenerative disease regulatory submissions comes from various sources. These include clinical trial reports, non-clinical study reports, pharmaceutical research data, epidemiological data, genomic data, and previous approval cases. Data update frequencies vary. Clinical trial data is continuously generated during trials, and literature updates as research progresses. Document structures are highly standardized, following guidelines such as ICH M4E. Documents are typically in PDF, Word, or Excel formats. Files contain extensive structured and semi-structured data, such as study protocols, statistical analysis plans, adverse event reports, laboratory test results, and patient characteristics. Field names are standardized and use many specialized terms, such as ADL score, MMSE score, CSF tau protein concentration, and APOE genotype. Units typically follow international standards or common clinical units, such as ng/mL, mmol/L, and mg/kg.

Constraints Imposed by These Characteristics on Multiturn Conversation and Prompts

The specialized and highly structured nature of neurodegenerative disease data requires a multiturn conversation system to accurately identify domain-specific terminology and data metrics when understanding user intent. The conversation system needs to handle complex logical relationships. For example, evaluating drug efficacy may require considering both the trend of MMSEMinute Number changes and improvements in the ADL量表. The standardized document structure means prompt design should leverage document hierarchy and section titles to improve information recall accuracy. Due to varying data update frequencies, the system needs version management capabilities to ensure it always references the latest or specified data version in multiturn conversations. Additionally, a large amount of semi-structured tabular data requires prompts to guide the model in precisely parsing and comparing table contents, such as extracting the adverse event rate for a specific dosage group.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`2048` Token	Neurodegenerative disease submission data has strong contextual relevance, requiring a longer conversation history to maintain context.
`Chunk size` (Chunk Size)	`500` characters	Documents contain extensive specialized descriptions and arguments. Chunks that are too short can break semantic integrity, while those that are too long may introduce irrelevant information.
`Recall count` (Recall Count)	`8–12` entries	This ensures coverage of multi-dimensional information while avoiding the recall of too much redundant or low-relevance content, which increases model processing burden.
`Similarity threshold` (Similarity Threshold)	`0.78`	Neurodegenerative disease terminology is highly specialized. Increasing the threshold filters out semantically similar but imprecise recall results.
`Rerank result count` (Reranked Return Count)	`5` entries	Based on a high recall count, reranking selects the most relevant segments that best support the answer, improving final output quality.
`systemPrompt`	Instructions including a glossary of specialized terms and abbreviations	This ensures the model accurately understands specialized neurodegenerative disease vocabulary, reduces ambiguity, and improves conversation efficiency.

Three Common Mistakes

Phenomenon: After a user query, the system provides a generalized answer without citing specific document content. Reason: The Similarity threshold (similarity threshold) is set too low, leading to the recall of many document segments with low relevance to the question. The model struggles to identify core information from these.
Phenomenon: In multiturn conversations, the model fails to correctly understand user questions about tabular data, such as "Compare the ADL scale differences in different dosage groups across two clinical trials." Reason: Prompts provide insufficient guidance for parsing tabular data. The model does not process table content as structured information.
Phenomenon: The output of a previous AI conversation in the workflow contains excessive redundant information, leading to inefficient processing by the subsequent AI conversation. Reason: The outputFormat or systemPrompt of the previous AI conversation does not explicitly request concise output, resulting in the transfer of unnecessary context.

How to Confirm Proper Configuration

Conduct multiturn conversation tests. Verify the system can accurately answer queries involving key metrics such as MMSE score and CSF tau protein, and can trace these answers to specific document sources.
Simulate complex user questions involving data comparison and trend analysis. Check if the system can correctly parse and provide logical answers, and verify the accuracy of cited data.
Test the retrieval and citation of different document versions. Ensure the system can distinguish and correctly call specific versions of data in multiturn conversations, for example, "Based on the 2023 version of the data, provide a risk assessment for APOE4 carriers."

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.