Multi-Turn Conversations and Prompts for mRNA Vaccine

Data Characteristics

mRNA vaccine regulations and Standard Operating Procedure (SOP) documents originate from official pharmaceutical regulatory bodies, internal pharmaceutical company policies, industry association guidelines, and international regulatory documents. These documents have a low update frequency, typically revised annually or periodically based on policy adjustments or technological advancements. Document structures are primarily PDF, Word, or internal knowledge base pages. They contain extensive specialized terminology, legal provisions, charts, and flowcharts. Fields and units are highly standardized, such as batch numbers, production dates, expiration dates, storage temperatures (℃), and dosages (μg/mL), demanding extreme precision. Data is typically text-based, but SOPs often include procedural descriptions like operational steps and quality control points.

Constraints on Multi-Turn Conversations and Prompts

The low update frequency of mRNA vaccine regulation documents means knowledge base content is stable after initial construction, requiring infrequent real-time synchronization. However, accurate validation during initial import is critical. The extensive specialized terminology and legal provisions in these documents require the model to have high-precision semantic understanding in multi-turn conversations to avoid ambiguity. The presence of flowcharts and operational steps means simple text retrieval is insufficient for question answering. The model must extract and integrate procedural details from structured information. Highly standardized fields and units require prompts to guide the model to focus on key values and specifications, ensuring rigorous answers. Additionally, due to compliance requirements, conversation results must be traceable to the original document source.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk Size`	`500–800 characters`	Ensures each knowledge chunk contains sufficient context while avoiding redundancy and comprehension difficulties from excessive length.
`Recall Count`	`Top 5`	Given the rigor of regulatory documents, increasing the recall count improves coverage and reduces the risk of overlooking critical information.
`Similarity Threshold`	`0.85–0.9`	Ensures recalled results are highly relevant to the user's query, filtering out low-quality, vaguely matched content. This is especially important for high-precision regulatory Q&A.
`maxContext`	`4000 tokens`	mRNA vaccine regulation Q&A often requires a longer conversation history and contextual understanding. This range balances efficiency and accuracy.
`Rerank Return Count`	`3`	Based on a high recall count, reranking selects the most relevant items, optimizing the quality of the final answer presented to the user.
`Opening Remarks Preset Questions`	`3–5 relevant questions`	Guides users to quickly focus on specific aspects of regulations or SOPs, for example, "What are the quality control procedures for mRNA vaccine production?"

Common Pitfalls

The conversation window displays "Knowledge base retrieval failed" or returns an empty value. This occurs when complex charts or flowcharts in PDF documents are not effectively OCR-processed or structured during dataset import.
After a user query, the model provides a generic answer, unable to offer specific values or steps. This happens when prompts do not explicitly require the model to extract and cite specific field values or step descriptions from the original documents.
During a conversation, the model repeatedly asks for already provided information or fails to understand specialized terminology in the user's query. This is due to an excessively small maxContext setting for the context window, leading to loss of multi-turn conversation history.

How to Verify Configuration

Conduct end-to-end tests for typical regulatory or SOP Q&A scenarios. Check if the model accurately cites specific legal provisions or operational steps from documents and compare the answer's consistency with the original text.
Test multi-turn conversations of varying complexity. Observe if the model maintains contextual coherence and progressively refines answers based on conversation history. Evaluate the effectiveness of the context window.
Review knowledge base retrieval logs. Confirm if Recall Count and Similarity Threshold effectively filter out irrelevant content and recall sufficient potentially relevant knowledge chunks in actual queries.
Simulate user queries containing typos or synonyms. Evaluate the model's robustness to text, ensuring it can still provide effective answers with imprecise input.

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