Multi-turn Conversations and Prompts for siRNA Nucleic Acid

Data Characteristics

siRNA nucleic acid drug data originates from preclinical research reports, clinical trial data, patent literature, regulatory agency (e.g., FDA, EMA) approval documents, and academic journals. This data updates infrequently, typically with new drug development progress or regulatory policy changes. Document structures are complex, covering drug mechanisms of action, target information, sequence design, administration methods, pharmacokinetics, toxicology, indications, side effects, and clinical efficacy. Fields often include nucleic acid sequences (e.g., sense strand, antisense strand), chemical modification sites, target gene IDs, disease codes (e.g., ICD-10), drug dosages (units mg/kg or nM), efficacy indicators (e.g., knockdown efficiency percentage), and adverse event grades (e.g., CTCAE standards). The data contains extensive unstructured text, such as experimental method descriptions and discussions.

Constraints on Multi-turn Conversations and Prompts

The complexity and specialized nature of siRNA nucleic acid drug data impose specific requirements on multi-turn conversation and prompt design. First, the accuracy of core information like nucleic acid sequences and target genes is critical. The conversation system must precisely identify and cite these specialized terms to avoid misunderstandings. Second, the low data update frequency means knowledge base construction requires authoritative and timely data sources to avoid outdated information. Third, complex document structures necessitate prompts that guide the model to extract key information from unstructured text, such as efficacy data at specific dosages from clinical reports. Multi-turn conversations must support users in deeply exploring drug mechanisms of action or side effects. The system should track context and provide more detailed explanations or relevant data in subsequent turns. For example, when a user asks about a specific siRNA's target, the system should provide the target name, explain its role in the disease pathway, and link it to the drug's clinical indications.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`Chunk size` (Chunk Size)	`800–1200 characters`	siRNA nucleic acid drug documents contain many specialized terms. Longer chunks help maintain semantic integrity of the context and prevent key information from being truncated.
`Recall count` (Recall Count)	`top 5–8 chunks`	Ensures retrieval of sufficient relevant document chunks, covering drug mechanisms, sequence details, and clinical data across multiple dimensions.
`Similarity threshold` (Similarity Threshold)	`0.75–0.85`	Ensures retrieved documents are highly relevant to the user's query, filtering out general biological information that appears related but is not precise within the nucleic acid drug domain.
`maxContext`	`32000 tokens`	A longer context window helps the model track complex nucleic acid drug development processes, pharmacokinetic data, and clinical trial results in multi-turn conversations.
`Rerank result count` (Reranked Return Count)	`3 chunks`	Focuses on the most core and relevant document chunks, improving the model's efficiency in extracting key information and avoiding interference from irrelevant data.
`temperature`	`0.3–0.5`	Ensures the model's drug consultation output is rigorous and accurate, reducing the risk of hallucination, and meeting the professional requirements of the biomedical field.

Common Pitfalls

The conversation system fails to correctly update global variables when a user repeatedly modifies a drug parameter (e.g., dosage or target gene), leading subsequent responses to still be based on old parameters. This occurs due to a lack of clear guidance for variable update instructions in prompt design, or the context management module not correctly handling variable overwriting logic.
A user asks about the clinical trial progress of a specific siRNA drug, but the system returns patent application information or early in vitro experimental data. This typically results from insufficient granularity in the knowledge base indexing, which cannot differentiate between document types at various development stages, leading to imprecise recall results.
When explaining the siRNA mechanism of action, the model outputs specialized terms that do not match the user's query domain, for example, explaining RNA interference as gene editing. This is due to term confusion in the training data or knowledge base, or the prompt failing to clearly define the model's answer scope and professional vocabulary usage guidelines.

Verification Steps

Test whether the model can accurately cite nucleic acid sequences (e.g., siRNA sequence) and target gene IDs for siRNA nucleic acid drug queries of varying complexity.
In multi-turn conversations, check if the system can provide coherent and accurate supplementary information based on previous turns' context when a user asks follow-up questions about a drug's dosage or indications, and distinguish whether the user is modifying parameters or asking for additional information.
Verify whether the system, when handling inquiries about drug side effects or clinical risks, can recall and summarize authoritative regulatory agency reports (e.g., FDA label) or clinical research data from the knowledge base, avoiding vague generalizations.
Evaluate whether the model can proactively clarify intent when faced with ambiguous queries, for example, asking the user if they want to know about the drug's in vitro effects or in vivo clinical data.

The values provided 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.