Autoimmune Product Multi-Turn Conversation and Prompting

Autoimmune Product Data Characteristics

Autoimmune product data originates from clinical trial reports, drug inserts, research papers, disease treatment guidelines, and pharmaceutical company product manuals. Data update frequencies vary. Clinical trial data and research papers might update quarterly or semi-annually, while drug inserts and treatment guidelines typically see annual revisions due to regulatory requirements. Document structures are standardized. Product inserts usually include sections like indications, dosage and administration, contraindications, and adverse reactions. Clinical reports cover study design, subject characteristics, efficacy endpoints, and safety assessments. Common fields and units include dosage (mg/kg), dosing frequency (times/week), remission rate (%), adverse event rate (%), and specific biomarker levels (e.g., autoantibody titers, cytokine concentrations, with units like IU/mL, pg/mL).

Constraints on Multi-Turn Conversation and Prompting

The diverse sources and varying update frequencies of autoimmune product data require the RAG system to efficiently retrieve and integrate information in multi-turn conversations, ensuring real-time accuracy. Standardized document structures improve information extraction accuracy, but potential inconsistencies in terminology and units across different sources must be handled. For example, different studies might use varying autoantibody detection methods or reporting units. Moreover, the complex diagnostic criteria and treatment pathways within disease guidelines demand high logical reasoning capabilities from multi-turn conversations. The dialogue system needs to understand multi-turn follow-up questions regarding patient symptoms and medication history, and accurately cite relevant knowledge points based on context to avoid misleading information due to ambiguity. Precise identification of fields and correct interpretation of units are especially critical when dealing with dosage and adverse reactions.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`2000 characters`	Ensures the context window is large enough to cover descriptions of symptoms and medication history in multi-turn conversations, while avoiding excessive length that could reduce model processing efficiency.
`Chunk size`	`500 characters`	Accommodates the typical information block length in autoimmune product inserts and clinical reports, balancing recall precision with fragmentation.
`Recall count`	`8 entries`	Considers the complexity of autoimmune diseases and the richness of the knowledge base content, ensuring coverage of multi-faceted information.
`Similarity threshold`	`0.75`	Addresses the specialized and precise terminology requirements of the biomedical field, improving the relevance of retrieval results.
`Rerank result count`	`3 entries`	Focuses on the core information most relevant to the user's current question, reducing redundancy and improving answer quality.
`AI Chat Model`	`gpt-4-turbo`	Handles the complex logical reasoning involved in autoimmune disease diagnosis and treatment plans, providing more accurate professional answers.

Common Pitfalls

The AI conversation module's output is directly displayed in the chat box instead of being concatenated as expected. This usually indicates a missing explicit "Specify Reply" or "Text Concatenation" component in the workflow design, causing the AI conversation's default output to be returned directly to the user.
The AI conversation module cannot correctly reference knowledge base retrieval results after they are passed to an HTTP request. This typically happens when the data format after HTTP request processing does not match the knowledge base reference format expected by the AI conversation module, preventing the AI from parsing or recognizing it.
In multi-turn conversations, the AI repeatedly asks for information already provided. This usually means maxContext is set too low, causing the model to forget earlier conversation history, or that information in the knowledge base is not effectively integrated, failing to support contextual understanding.

Validation Steps

Simulate user queries covering product indications, dosage and administration, and adverse reactions to verify if the AI can accurately cite knowledge base information and provide coherent answers in multi-turn conversations.
Examine conversation logs to confirm the completeness of historical conversation content within maxContext and whether the knowledge base's Recall count and Similarity threshold effectively filter relevant document snippets.
For specific diseases or products, test if the AI can accurately recommend relevant products or provide treatment advice based on user-provided symptoms or test indicators, and verify the accuracy and professionalism of the cited information.
Validate the input and output data formats of each module in the workflow to ensure smooth data flow, especially that knowledge base retrieval results, after processing by an HTTP request, are correctly received and parsed by the AI conversation module.

Note: The values provided are common starting points. Measure performance against your 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.