Multi-turn Conversation and Prompts for Infection Control

Data Characteristics for this Category

Infection control management regulations primarily originate from internal documents published by healthcare institutions. These include regulations, operational guidelines, emergency plans, and training manuals. Documents are typically in PDF, Word, or scanned image formats. Update frequency is relatively low, usually quarterly or annually, with ad-hoc updates for major policy changes or emergencies. Document structure is rigorous, containing directories, chapters, clauses, and attachments. They extensively use professional terminology and abbreviations. Fields and units involve specific operational steps, time requirements (e.g., disinfection duration 30 minutes), concentration ratios (e.g., disinfectant concentration 0.5%), personnel responsibilities, and equipment models. These often include explicit numerical values and unit specifications.

Constraints on "Multi-turn Conversation and Prompts" from these Characteristics

The rigorous structure and specialized terminology of infection control management documents require the question-answering system to accurately understand and identify user intent in multi-turn conversations. This prevents misinterpretation due to semantic ambiguity. Due to the low update frequency, the system must ensure the authority and timeliness of the knowledge base content, avoiding the citation of outdated regulations. Specific numerical values and units in documents, such as isolation time 48 hours or seven-step hand hygiene method, demand precision in prompt generation. The system needs to extract and present accurate numerical information from the original text. Additionally, multi-turn conversations may involve step-by-step guidance for complex operational procedures. This requires prompts to structure and guide the user through the query.

Configuration Settings

Configuration Item	Suggested Value	Rationale for this Value
`maxContext`	`8`	Infection control queries often involve multiple steps, requiring a longer conversational context.
`Segment Length`	`500 characters`	Regulatory documents are dense; longer segments help preserve semantic integrity.
`Recall Count`	`10 entries`	Increases the initial recall scope to cover more relevant regulatory clauses.
`Similarity Threshold`	`0.75`	Ensures precision of recalled content, filtering irrelevant or vague results.
`Rerank Return Count`	`3 entries`	Focuses on the most relevant content, reducing user reading burden and improving efficiency.
`SYSTEM_PROMPT`	Describes the role of an "Infection Control Management Expert," emphasizing rigor and compliance.	Ensures model output adheres to medical norms and professional requirements.

Three Common Mistakes

The system repeatedly responds with "No relevant regulations found." This happens when the Similarity Threshold is set too high. Even relevant content gets filtered out because it does not meet the strict matching criteria.
The system frequently loses context during multi-turn conversations. This occurs when the maxContext parameter value is set too low, preventing the model from remembering previous conversation turns.
A user asks about "disinfectant ratio," but the system returns "equipment maintenance procedures." This indicates that the prompt did not adequately guide the model to focus on the core intent of "ratio," or the SYSTEM_PROMPT lacked sufficient understanding of domain-specific terminology.

How to Confirm Proper Configuration

Select at least 10 common infection control management questions (e.g., "indications for hand hygiene," "cleaning and disinfection procedures for isolation wards"). Test these in multi-turn conversations to ensure the system provides accurate and coherent answers.
For questions involving specific numerical values and units (e.g., "surgical hand disinfection time," "medical waste temporary storage limit"), verify that the numerical values returned by the system match the original regulations. The error should be within an acceptable range.
Simulate a user gradually delving into a topic during a conversation. Check if the system maintains contextual coherence and adjusts recall results based on new questions.
Use the "Log Management" feature in the FastGPT backend. Observe the raw segment content recalled by the model during conversations. This helps determine if Recall Count and Similarity Threshold are reasonable and if a large amount of irrelevant information is being recalled.

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.