Data Characteristics for This Category
Antibody-Drug Conjugate (ADC) policy and SOP data primarily originate from guidelines and technical review requirements published by regulatory agencies (e.g., NMPA, FDA, EMA), as well as internal R&D, manufacturing, and quality management system documents from companies. This data typically exists in formats like PDF, Word, and XML. Document structures are complex, containing extensive specialized terminology, diagrams, and cross-references. Update frequency depends on regulatory revisions and internal process optimizations, usually quarterly or annually. Key fields include drug name, target, conjugation technology, payload, indications, clinical trial phase, batch information, and quality control standards. Units involve concentration (μg/mL), dosage (mg/kg), purity (%), and pH values.
Constraints Imposed by These Characteristics on "Multi-Turn Conversations and Prompts"
The complexity and specialized nature of ADC policy documents require higher semantic understanding and context retention for multi-turn conversations. The large volume of specialized terms and abbreviations demands accurate terminology parsing and concept association capabilities from the system to avoid ambiguity in multi-turn interactions. Frequent cross-references and diagrams in documents require the system to identify and guide users to relevant content. Furthermore, the update frequency of regulations and SOPs necessitates rapid synchronization of the knowledge base with the latest versions to ensure the timeliness and accuracy of conversation results. In multi-turn conversations, users may frequently inquire about quality control details for specific batches or formulations, requiring the system to precisely extract information from both structured data and unstructured text.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 8 turns | ADC policy Q&A often requires a longer context to understand complex questions and follow-up details. |
Chunk Length | 800–1200 characters | Considering ADC document paragraphs are long and information-dense, a longer chunk length helps maintain semantic integrity. |
Recall Count | Top 8 | Ensures coverage of multiple relevant policy clauses and SOP sections, improving recall accuracy. |
Similarity Threshold | Calibrated by measurement | ADC involves many specialized terms; testing against actual corpora is needed to balance recall and precision, e.g., 0.75. |
Reranked Return Count | Top 4 | After reranking, the most relevant document snippets are prioritized, enhancing answer quality. |
Prompt Template | Includes "As a professional ADC regulatory expert" | Defines the AI's role, guiding it to answer policy questions with a professional and rigorous tone. |
Three Common Mistakes
- Symptom: In a multi-turn conversation, the user asks about the quality standards for a specific batch, but the system provides generic content and fails to give specific batch standards. Reason: The knowledge base lacks fine-grained indexing for batch information, or the prompt does not explicitly require the AI to cite specific batch information in its answer.
- Symptom: The system's explanation of ADC specialized terms is inconsistent across multi-turn conversations, or it cannot understand user follow-up questions about abbreviations. Reason: The knowledge base lacks a unified glossary or ontological knowledge, leading to insufficient understanding of specialized vocabulary and context retention by the AI.
- Symptom: The user asks about the revision history of a policy clause, but the system cannot provide it or provides outdated information. Reason: The knowledge base fails to effectively manage document versions, or the indexing update mechanism is incomplete, resulting in the recall of non-latest document versions.
How to Confirm Proper Configuration
- Select multiple test cases containing key ADC terms and multi-turn follow-up questions. Run the conversation flow to verify if the system accurately understands the questions and provides relevant answers.
- Check the conversation history to confirm if the system maintains an understanding of ADC-related context across multi-turn interactions, such as continuous recognition of drug targets and conjugation methods.
- For frequently updated ADC regulations or SOPs, upload new document versions and immediately conduct tests to ensure the system can recall and cite the latest content.
- Test the system's ability to handle common diagrams and cross-references in ADC documents. For example, after asking "See Appendix A," can the system correctly guide the user?
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.