Multi-Turn Conversations and Prompts for Structured Analysis

Data Characteristics for This Category

Attenuated inactivated vaccine R&D documents typically include preclinical study reports, strain screening records, manufacturing process specifications, quality control standards, stability study data, and clinical trial protocols. Data sources are diverse, encompassing internal lab records, partner institution reports, and regulatory submissions. The update frequency is high, especially during clinical trial phases, where data is generated in real-time. Documents are often in PDF or Word format, with varying degrees of structure. They contain numerous biological terms, abbreviations, charts, and complex formulas. Fields cover viral titers, host cell lines, adjuvant components, immunogenicity indicators, and antibody titers, with specialized units such as TCID50/mL, PFU/mL, and μg/mL.

Constraints Imposed by These Characteristics on "Multi-Turn Conversations and Prompts"

The complexity of attenuated inactivated vaccine R&D documents challenges the accuracy and coherence of multi-turn conversations. High-frequency data updates require the knowledge base to rapidly synchronize and index information, ensuring the conversational model accesses the latest data. The prevalence of specialized terminology and abbreviations in documents necessitates prompt design that considers term standardization and contextual relevance to avoid ambiguity. The unstructured nature of charts and formulas makes direct information extraction difficult. Prompts must guide the model to identify key data points or instruct users to consult original charts. Additionally, tracing specific experimental results or production batch data in multi-turn conversations requires the model to precisely locate specific document sections or tables, making the settings for Recall count (recall count) and Similarity threshold (similarity threshold) particularly sensitive.

Configuration Settings

Configuration Item	Suggested Value	Rationale for This Value
`maxContext`	`2000 characters`	Vaccine R&D conversations have high information density, requiring a larger context window for coherence.
`Chunk size` (Segment Length)	`800–1200 characters`	Balances semantic completeness with information density per segment, adapting to complex document structures.
`Recall count` (Recall Count)	`Top 10–15 items`	Ensures coverage of multiple information points, addressing the low-frequency occurrence of specialized terms.
`Similarity threshold` (Similarity Threshold)	`0.78–0.85`	Balances recall precision and recall rate, reducing interference from irrelevant paragraphs.
`Rerank result count` (Reranked Return Count)	`Top 5 items`	Refines final output, focusing on core relevant information, and improving response speed.
`maxToken`	`4096`	Accommodates detailed experimental data descriptions and explanations.

Three Common Mistakes

In multi-turn conversations, the model fails to accurately remember specific experimental parameters or batch information from previous turns, leading to irrelevant answers in subsequent follow-up questions. This occurs because maxContext is set too small, or prompts do not effectively guide the model to focus on core entities in historical conversations.
When a user asks about "strain titer," the model returns content related to "immunogenicity," indicating a significant information deviation. This might be due to a Similarity threshold (similarity threshold) that is too high, leading to insufficient recall, or because the vector representations of "strain titer" and "immunogenicity" in the knowledge base are too close, lacking sufficient differentiation.
When a user requests to "read the code in directory XX," the model cannot perform the action and only provides a text response. This happens because external tools or plugins are not integrated, for example, an execution environment like open-interpreter is not configured, preventing the conversational model from directly triggering system-level operations.

How to Confirm Proper Configuration

Select typical questions covering different R&D stages (preclinical, clinical phase I, manufacturing) and conduct multi-turn conversation tests. Observe if the model can accurately cite and link key entities (e.g., specific strain numbers, experimental groups) from historical conversations after 3-5 turns.
For document sections containing charts and complex formulas, pose questions requiring data analysis from them. Check if the model can identify and prompt the user to consult the original charts, or find corresponding structured data in the knowledge base.
Randomly select 10 questions containing specialized terminology and abbreviations. Compare the model's results with human expert analysis to verify if key information points (e.g., viral titer TCID50/mL, antibody titer ELISA IU/mL) are consistent. Observe if Recall count (recall count) and Rerank result count (reranked return count) effectively support these answers.

Note: The values provided are common starting points. It is recommended to measure against your own samples for optimal performance.

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.