Data Characteristics in this Domain
Autoimmune disease R&D documents come from diverse sources. These include clinical trial reports, basic research papers, patent literature, drug molecule structure data, gene sequencing results, and patient cohort data. Document update frequencies vary; clinical trial data updates continuously during trials, while basic research findings are published as research progresses. Document structures are complex, often containing unstructured text (e.g., research background, discussion), semi-structured tables (e.g., drug dosage, adverse event statistics), and structured data (e.g., gene sequences, protein structures). Fields and units are highly specialized. For example, dosage units may involve mg/kg, µg/mL, time units may involve weeks, months, and biomarker indicators include CRP, ESR.
Constraints Imposed by these Characteristics on Multiturn Conversation and Prompts
The complex data characteristics of autoimmune R&D documents impose specific requirements on multiturn conversation and prompt design. Documents contain numerous specialized terms and abbreviations. Prompts must effectively guide the model to identify and interpret these terms to avoid misunderstandings. Multiturn conversations require handling cross-document references and contextual associations. For example, discussing a drug's efficacy may require tracing its performance across different clinical trials. Semi-structured table data requires the model to extract specific information from complex tables. Prompt design must clearly specify target fields and extraction logic. Additionally, the asynchronous nature of data updates means the knowledge base needs regular synchronization with the latest information. The dialogue system must explicitly state data timestamps when citing information to avoid providing outdated information.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for this Value |
|---|---|---|
maxContext | 3000 Tokens | Ensures the model can accommodate multiturn conversation history and retrieved long professional document snippets, while balancing inference efficiency. |
Chunk size (Segment Length) | 500 characters (characters) | Accommodates longer paragraphs and complex sentence structures in R&D documents, ensuring semantic completeness and preventing key information truncation. |
Recall count (Recall Count) | Top 8 entries (top 8) | The autoimmune domain has high information density. Increasing the recall count improves the probability of retrieving relevant specialized knowledge. |
Similarity threshold (Similarity Threshold) | 0.75 | Domain-specific terms have high similarity. A higher threshold is needed to precisely match relevant concepts and reduce noise. |
Rerank result count (Reranked Return Count) | Top 5 entries (top 5) | After reranking, selecting the top few most relevant results balances information comprehensiveness and model processing load. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Handles complex parsing tasks for large clinical trial reports or research papers, allowing sufficient file processing time. |
Three Common Mistakes
- Model output for drug names or gene sequences contains formatting errors, such as extra spaces or case inconsistencies. This occurs because prompt constraints on output format are not clear enough, and the model fails to strictly adhere during generation.
- During conversation, the model cannot link related data across different documents, leading to fragmented information. This happens because the knowledge base segmentation strategy may be too granular, or the retrieval algorithm fails to effectively capture cross-document semantic associations.
- Uploading large research report files results in a prolonged system unresponsiveness or errors. This is because file processing parameters like
PARSE_FILE_TIMEOUT_SECONDSare set too low and do not cover the parsing time required for complex documents.
How to Confirm Proper Configuration
- Upload typical clinical trial reports and basic research papers on autoimmune diseases. Conduct multiturn questioning to verify if the model can accurately extract key data, such as drug dosages and adverse event rates.
- Test queries containing specialized terms and abbreviations. Observe if the model can correctly explain their meanings and cite relevant definitions from the knowledge base. Cross-check the model's understanding of terms like
CRPandTNF-α. - Simulate users asking about the latest developments for the same disease at different times. Check if the model can identify document update times and prioritize citing the most recent data. Confirm the knowledge base synchronization mechanism is effective.
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.