Data Characteristics
CSO (Chief Scientific Officer) R&D document data primarily comes from internal experimental reports, preclinical study data, patent literature, regulatory documents, and external scientific journals. Update frequency depends on R&D project progress, typically occurring at key research milestones or data releases. Document structures vary, including structured experimental data tables, semi-structured research reports (with introduction, methods, results, discussion), and unstructured text descriptions. Fields involve compound structures, target information, pharmacokinetic parameters, toxicology data, and clinical trial indicators. Units include molar concentrations (nM, µM), dosages (mg/kg), time (h, days), and activity (IC50, EC50), covering various biochemical and pharmacological units.
Constraints on Multi-turn Conversation and Prompts
The highly specialized and diverse nature of CSO R&D document data challenges multi-turn conversation context management. Complex compound structures and specialized terminology require the dialogue system to accurately understand and maintain semantic consistency. Pharmacokinetic and toxicology data often appear in tables, requiring effective citation and explanation of specific values and trends in conversation. The dynamic and multi-dimensional nature of clinical trial indicators means prompt design must guide users to explore relationships between different parameters. Furthermore, the rigor of regulatory documents and the specific format of patent literature require the system to distinguish factual statements, data citations, and compliance explanations when generating responses, ensuring accuracy and traceability. Dialogue context needs long-term memory to support complex cross-document and cross-concept question tracing and answering.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 12 turns | Ensures conversations cover multi-step exploration of complex R&D issues |
promptTemplate | includes {{context}} and {{query}} | Guarantees the system effectively integrates historical conversations with current user intent |
temperature | 0.3–0.5 | Prioritizes response accuracy and objectivity, reducing hallucination risk |
similarityThreshold | 0.78 | Precisely recalls highly relevant R&D document segments |
top_k | 5 entries | Balances recall breadth and computational efficiency, focusing on key information |
chunkSize | 800 characters | Accommodates longer paragraphs in R&D reports, reducing semantic fragmentation |
Common Mistakes
- The dialogue frequently returns "No relevant information found" or overly generic responses. This usually happens when
similarityThresholdis set too high ortop_kis too low, preventing the system from recalling enough relevant document segments. - When users ask for specific compound pharmacokinetic parameters, the response fails to provide concrete values or units. This may be because document chunking did not adequately preserve table or key field context, or the prompt did not explicitly ask the model to extract and present specific data with units.
- In multi-turn conversations, the system loses memory of earlier questions, requiring users to repeat information. This typically indicates insufficient
maxContextconfiguration, leading to truncated historical conversations and ineffective context transfer.
How to Verify Configuration
- Conduct multi-turn dialogue tests for typical R&D questions. Observe if the system maintains understanding of early topics after 5-8 turns.
- Randomly select 10-15 test questions. Check if the system's responses accurately cite specific values, units, and specialized terminology from documents, and verify consistency with original documents.
- Simulate user follow-up questions on specific experimental results or regulatory clauses. Evaluate if the system can provide in-depth and logically coherent explanations without introducing hallucinations, verifying compliance.
The values provided are common starting points and should be measured against your 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.