Data Characteristics
Bispecific antibody pharmacovigilance data originates from clinical trial reports, real-world evidence (RWE), post-market surveillance systems (e.g., FDA Adverse Event Reporting System, FAERS; European Medicines Agency EudraVigilance), and medical literature. This data typically combines unstructured text (e.g., case reports, physician notes), semi-structured data (e.g., adverse event reporting forms), and structured data (e.g., drug names, adverse reaction terms, dosage, administration routes, start/end times). Data updates frequently, especially during initial drug launch and clinical research phases. Document structures are complex, potentially including multi-center, multi-language reports involving medical terminology, abbreviations, and disease codes (e.g., MedDRA terms). Specific fields, beyond standard patient information, drug information, and adverse event descriptions, include target binding mechanisms, antibody domains, and immunogenicity test results. These specific fields are crucial for understanding adverse reaction mechanisms. Units cover dosage (mg/kg), time (days, weeks, months), and biomarker concentrations (ng/mL).
Constraints on Multi-turn Conversations and Prompts
The multimodal nature and complexity of bispecific antibody pharmacovigilance data impose specific constraints on multi-turn conversation and prompt design. Unstructured text descriptions of adverse events require strong natural language understanding to accurately extract key information. Specific fields in semi-structured and structured data (e.g., antibody domains) require prompts to guide the model to focus on this high-value information and perform relational analysis. High-frequency data updates mean the knowledge base needs rapid synchronization and incremental updates; otherwise, conversations may rely on outdated information. Complex medical terminology and coding systems, such as MedDRA terms, require the model to correctly parse and generate information, avoiding ambiguity. Multi-turn conversations need context coherence. For example, if a user asks about a specific adverse reaction in one session, subsequent conversations may need to track its dose-dependency or interactions with other drugs. This requires prompts to effectively manage and utilize historical conversation information. Additionally, privacy-sensitive information in adverse event reports requires instructions for de-identification or anonymization in prompt design.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8192 token | Accommodates complex case texts and multi-turn conversation context needs, balancing model inference cost and effectiveness. |
Chunk size | 500 characters | Ensures each segment contains sufficient context while preventing single segments from becoming too long and diluting information density. |
Recall count | Top 10 entries | Increases the probability of recalling relevant knowledge, covering potentially dispersed key information in pharmacovigilance reports. |
Similarity threshold | 0.75 | Filters for knowledge snippets highly relevant to bispecific antibody pharmacovigilance queries. |
Rerank result count | Top 5 entries | Prioritizes displaying core information most relevant to user intent, improving response efficiency. |
promptTemplate | Contains MedDRA | Explicitly instructs the model to consider and apply MedDRA terms in its responses, enhancing professionalism. |
Common Pitfalls
- Symptom: AI responses contain non-professional terms or descriptions inconsistent with medical reports. Reason: Prompts do not explicitly require the model to adhere to specific medical dictionaries or standards, leading to the model generating free-form text.
- Symptom: In multi-turn conversations, the AI fails to recall detailed information about specific adverse events from previous turns. Reason: The
maxContextparameter is set too low, causing historical conversation context to be truncated, and the model cannot access complete information. - Symptom: Prompts or guiding words do not influence the model's response, and the model's behavior deviates from expectations. Reason: Model selection has compatibility issues with the prompts, or the weight of the prompts is overridden by other internal model factors.
Validation
- For a series of typical bispecific antibody adverse event queries, verify whether AI responses accurately cite drug names, dosages, and adverse reaction terms from the knowledge base.
- Perform multi-turn conversation tests where later questions depend on drug or adverse reaction details mentioned in earlier turns. Check if the AI maintains contextual coherence.
- Submit queries containing MedDRA codes to verify if the AI can correctly parse and use these professional codes in its responses, or follow their structure when generating text.
Note: The values provided are common starting points. Measure performance against your own samples to determine optimal settings.
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.