Multi-Turn Conversations and Prompts for mRNA Vaccines

Data Characteristics

mRNA vaccine data primarily originates from clinical trial reports, regulatory approval documents, academic papers, patent documents, and drug inserts. This data updates frequently, especially during the research and initial market release phases. Document structures typically include detailed pharmacology, clinical pharmacokinetics, indications, dosage and administration, adverse reactions, contraindications, drug interactions, and storage conditions. Key fields include specific antibody titers, neutralizing antibody levels, protection efficacy percentages, adverse event rates, dosage units (e.g., μg), and administration intervals (e.g., 28 days). Batch numbers, expiration dates, and manufacturer information are also common tracking fields.

Constraints on Multi-Turn Conversations and Prompts

The high update frequency of mRNA vaccine data requires the knowledge base to quickly synchronize and index new information. Failure to do so can lead to multi-turn conversations citing outdated or incorrect information. The complex structure of clinical trial reports and regulatory documents challenges document chunking and vectorization quality. Improper chunking can fragment critical information, affecting semantic recall. The presence of specific fields (e.g., antibody titers, protection efficacy) requires prompt design to effectively guide the model in understanding and utilizing these numerical values for reasoning and comparison. For instance, comparing the efficacy of different vaccine doses or batches requires the model to accurately extract and compare numerical values like 保护效力百分比. Conversations often involve safety and side effects. This requires prompts to differentiate the severity and frequency of adverse events when recalling relevant information, avoiding generalization or misinformation.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size`	`400–600 characters`	Clinical trial reports often contain multiple parallel observations. This length helps maintain the integrity of a single conclusion.
`Recall count`	`Top 8 entries`	This ensures coverage of multiple dimensions, including clinical research, adverse reactions, and dosage, for complex queries.
`Similarity threshold`	`0.78–0.85`	This balances recall precision and recall rate, reducing interference from irrelevant information while avoiding missing critical details.
`Rerank result count`	`Top 5 entries`	This improves the readability and response speed of key information in multi-turn conversations, focusing on the most relevant content.
`maxContext`	`6000 tokens`	This accommodates lengthy background descriptions and details found in mRNA vaccine product inserts or research reports.
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	This handles large PDF-format clinical trial reports, ensuring file parsing completes.

Common Pitfalls

Conversations cite outdated or withdrawn batch information. This occurs because the knowledge base content is not updated in time, leading the model to use old data.
When users ask to compare the protection efficacy of different mRNA vaccines, the model cannot provide clear numerical comparisons. This happens because prompts do not explicitly instruct the model to extract and compare numerical fields like 保护效力百分比.
Uploading large PDF-format clinical trial reports results in a File Parsing Timeout error. This likely occurs because the PARSE_FILE_TIMEOUT_SECONDS parameter is set too low, failing to handle the file size and complex structure.

Verification Steps

Ask about the latest approval status and expiration dates for different mRNA vaccine batches. Check if the model accurately cites content from the most recent regulatory documents.
Simulate user queries about the 保护效力百分比 and common adverse event rates for a specific vaccine dose. Verify if the model's output values match the original documentation.
Upload an mRNA vaccine clinical research report containing multiple tables and charts. Observe if the system successfully parses it and incorporates its content into the knowledge base.
Conduct multi-turn follow-up questions. For example, first ask about vaccine indications, then ask about its contraindications in specific populations. Check if the model maintains context and answers accurately.

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.