Multi-turn Conversations and Prompts for Infectious Disease

Data Characteristics in this Category

Infectious disease product and reagent information comes from diverse sources. These include product inserts, clinical research reports, industry standards, academic papers, and regulatory documents. Data update frequencies vary. New product launches, revised clinical guidelines, and the release of viral mutation information can all trigger updates. Significant updates typically occur quarterly or semi-annually.

Regarding document structure, product inserts usually contain structured or semi-structured fields such as product name, lot number, indications, detection principle, operating procedures, result interpretation, and precautions. Clinical research reports are more complex, involving study design, subject information, statistical analysis methods, and detailed experimental data.

Common fields and units include detection limits (e.g., IU/mL, copies/mL), sensitivity, specificity (percentage), storage conditions (e.g., 2-8°C), shelf life (years/months), and genotype or serotype information for specific pathogens.

Constraints on Multi-turn Conversations and Prompts

The high specificity and update frequency of infectious disease data impose specific requirements on multi-turn conversation and prompt design. Prompts must accurately identify and parse specialized terminology and abbreviations (e.g., HIV-1, HBsAg) found in product inserts. Frequent data updates mean the knowledge base needs regular synchronization with the latest information. Failure to do so can lead to outdated or inaccurate product information in conversations.

The coexistence of structured and semi-structured data in documents requires prompts to precisely locate specific fields and understand contextual meaning during information extraction. For example, when a user asks about "the detection limit of a certain kit," the prompt needs to differentiate detection limit values for different pathogens or detection methods.

Given the clinical application context, conversation accuracy and rigor are crucial. Prompt design must avoid ambiguity and ensure that multi-turn interactions consistently focus on professional product attributes and technical details. An example is distinguishing the subtle difference between "detection results" and "result interpretation."

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size`	`800–1200 characters`	Infectious disease product inserts often have long paragraphs describing detection principles or operating procedures. This length preserves semantic integrity.
`Recall count`	`8–12 entries`	Ensures coverage of multiple relevant document segments, such as product inserts and clinical reports, increasing information comprehensiveness.
`Similarity threshold`	`0.75–0.85`	Domain terminology is highly specific. Increasing the threshold reduces recall of irrelevant information, focusing on core issues.
`Rerank result count`	`3–5 entries`	After re-ranking, focuses on the most relevant pieces of information, reducing the model's processing burden and improving response efficiency.
`maxContext`	`4096 tokens`	Accommodates increased context length in multi-turn conversations, especially in complex product consultation scenarios.
`temperature`	`0.3–0.5`	Ensures rigorous and objective responses, avoiding the generation of inaccurate or speculative medical information.

Common Mistakes

Symptom: User asks about "the shelf life of a certain kit," and the AI provides an empty or incorrect date. Reason: The prompt failed to effectively identify the correlation between product batch and shelf life, or the knowledge base lacked this field.
Symptom: In multi-turn conversations, the AI cannot use context to understand that "it" refers to a previously mentioned pathogen or kit. Reason: The prompt did not adequately guide the model for anaphora resolution, or maxContext was set too low, leading to context truncation.
Symptom: maxContext is set to 0 in the workflow, causing every conversation to behave like a first-time query. Reason: Setting maxContext to 0 means no historical chat records are passed, preventing the model from engaging in multi-turn conversations.

How to Verify Configuration

For core products or reagents, ask multi-turn questions to check if the AI can accurately identify and answer key information such as product name, lot number, and detection limit. Ensure topic consistency during follow-up questions.
Randomly select frequently updated documents from the knowledge base (e.g., the latest product insert). Ask about new or modified content to verify timely knowledge base synchronization and the prompt's ability to understand new information.
Simulate a user query that includes specialized terminology or abbreviations. Observe if the AI's response correctly parses these terms and provides a professional answer consistent with expectations. Also, check for hallucinated content in the response.

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.