Data Characteristics
Target discovery data is highly specialized and complex. Data sources include public databases (e.g., UniProt, KEGG, PDB, ClinicalTrials.gov), scientific papers, patent literature, internal company experimental reports, and preclinical research data. Data update frequencies vary; public databases typically update quarterly or annually, while internal experimental data is generated in real-time. Document structures are diverse, covering structured data (e.g., gene sequences, protein structures, compound information) and unstructured text (e.g., experimental methods, results analysis, discussion). Fields and units are highly specific, such as gene ID, protein ID, EC number, IC50 value (nM), Kd value (nM), half-life (h), and dosage (mg/kg). These require precise identification and parsing.
Constraints Imposed by These Characteristics on Multi-turn Conversations and Prompts
The specialized and diverse nature of target discovery data imposes specific requirements on multi-turn conversation and prompt design. First, the wide range of data sources means the model must process multimodal information and extract key information from various sources. Second, differing update frequencies require the Retrieval-Augmented Generation (RAG) system to have flexible data indexing and refresh mechanisms to ensure conversations are based on the latest information. The complexity of document structures requires prompts to guide the model in understanding the context of different document types, for example, distinguishing between experimental methods and results. The specificity of fields and units requires the model to accurately identify and use specialized terminology and numerical values in Q&A, avoiding generalization errors. Additionally, due to sensitive research and development information, the conversation system needs high accuracy and traceability to support the rigor of registration documentation.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 8000 tokens | Covers lengthy experimental reports and multiple literature abstracts, maintaining contextual coherence. |
Chunk size (Chunk Size) | 500 characters (characters) | Balances semantic completeness and retrieval efficiency, avoiding truncation of critical information. |
Recall count (Recall Count) | Top 10 entries (top 10) | Ensures enough relevant segments are retrieved to cover specialized terminology. |
Similarity threshold (Similarity Threshold) | 0.78 | Balances recall rate and accuracy, filtering out irrelevant research data. |
Rerank result count (Reranked Return Count) | Top 5 entries (top 5) | Optimizes the final context passed to the LLM, focusing on the most relevant content. |
LLM_MODEL_NAME | gpt-4o-mini | Balances complex reasoning capabilities with cost-effectiveness, suitable for specialized Q&A. |
Common Pitfalls
Humanis null in the response record preview: This typically occurs because the frontend failed to correctly capture user input, or thechat_historyfield in the API request body is empty or malformed.- Poor AI dialogue box performance: Common reasons include a lack of preprocessing for user input, such as not performing entity recognition or intent classification, which prevents prompts from guiding the model precisely.
- Model answers do not meet expectations: Prompts lack sufficient domain-specific constraints or negative examples, leading the model to generate generalized answers that do not incorporate specialized knowledge in target discovery.
Verification Steps
- For typical target information queries, verify whether key specialized fields, such as IC50 and Kd values, in the model's output match the original document data.
- Simulate multi-turn follow-up questions from a user. Check if the model maintains contextual coherence and dynamically adjusts its answering strategy based on previous turns, for example, by asking about mutation information for a specific gene.
- Test data from different sources (e.g., UniProt entries, patent abstracts) to verify if the model can accurately extract and integrate information across various document structures.
- Evaluate the model's ability to correctly understand and generate answers that conform to biomedical domain standards by inputting complex questions containing specialized terminology, such as explaining the regulatory mechanism of a specific signaling pathway.
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.