Data Characteristics for This Category
Autoimmune disease registration data comes from various sources. These include clinical trial reports, non-clinical study reports, pharmacology and toxicology data, manufacturing process documents, quality standards, and guidance from domestic and international regulatory agencies. Public information on approved drugs is also used. Data updates are relatively slow, mainly occurring during new drug development and post-market changes. Document structures are highly standardized, following common formats like ICH M4E. They contain sections such as abstracts, main text, and appendices. Field content covers patient demographics, disease activity indicators (e.g., SLEDAI, DAS28), biomarkers (e.g., autoantibody titers, cytokine levels), adverse event classifications (MedDRA codes), drug dosages, and efficacy evaluation metrics. Units are clearly defined, such as mg/kg, IU/mL, and pg/mL.
Constraints Imposed by These Characteristics on "Multiturn Conversation and Prompts"
The standardized structure and clear fields of autoimmune data allow for precise query path construction when extracting specific information in multiturn conversations. Due to the low frequency of data updates, the conversation system does not need to refresh the knowledge base index often. This allows more computational resources to be allocated to deep semantic understanding. The complexity of biomarkers and disease activity indicators requires prompt design to effectively guide the model in understanding numerical ranges and clinical significance within the context, preventing misjudgments. Additionally, the MedDRA coding system for adverse events requires the model to recognize and associate these specialized terms, ensuring the accuracy of registration documents. Multiturn conversations need to support cross-referencing and logical reasoning across different document types (e.g., CTD Modules 2, 3, 4, 5) to answer comprehensive questions.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 800–1200 characters | Ensures the model can cover long clinical trial report summaries and key non-clinical data while controlling computational overhead. |
top_k | 5 segments | Autoimmune data has high information density; recalling a few highly relevant segments provides core information. |
rerank_top_n | 3 segments | Further refines recall results, prioritizing document segments that best match the current conversation intent. |
temperature | 0.2 | Registration documents demand high accuracy. A low temperature value reduces model "creativity" and increases objectivity. |
embedding_model | text-embedding-ada-002 or equivalent | Ensures semantic understanding of biomedical terminology and complex sentence structures. |
prompt_template | Include "Based on the provided autoimmune disease registration documents..." | Clearly defines the model's response scope, avoiding external knowledge and focusing on document content. |
Three Common Mistakes
- The conversation model fails to recognize the normal range or abnormal thresholds of specific biomarkers, leading to incorrect interpretation of clinical significance. This occurs when prompts do not sufficiently emphasize the contextual relevance of these key values, or the knowledge base does not effectively extract this information.
- When retrieving adverse events, the model cannot correctly associate MedDRA codes with corresponding clinical manifestations, resulting in missing or confused information. This happens when the knowledge base index inadequately parses the coding system, or prompts do not guide the model in code conversion.
- When users switch AI models, they find that prompt words or context information is lost, preventing the new model from continuing the previous conversation. This happens when the conversation application layer does not correctly persist and pass the
session_idorhistoryparameters.
How to Confirm Correct Configuration
- Submit queries containing specific disease activity indicators and biomarker values. Check if the model accurately determines their clinical significance and cites relevant documents for support.
- Input MedDRA codes or their corresponding clinical descriptions. Verify if the model can bidirectionally identify and provide accurate adverse event information.
- In multiturn conversations, switch between different AI models. Verify that
maxContextandhistoryparameters are correctly passed, ensuring context continuity. - For registration documents from different modules (e.g., Modules 2, 3, 4, 5), test whether the model can accurately answer cross-module comprehensive questions, such as the relationship between drug mechanism of action and clinical efficacy, without explicitly specifying the document source.
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.