Data Characteristics
Small molecule pharmaceutical regulatory data originates from drug administration regulations, internal quality management system documents (e.g., GMP, GCP, GLP SOPs), R&D and production records, and clinical trial protocols. These documents are typically in PDF, Word, or scanned image formats. Update frequency is relatively stable; regulations may revise annually, while internal SOPs update periodically based on business needs and regulatory changes. Document structures are rigorous, often including chapters, clauses, and annexes in standardized formats. Data fields cover drug names, batch numbers, manufacturing processes, quality standards, testing methods, storage conditions, expiry dates, adverse reactions, and operating procedures. Units include industry-specific measurements such as mg, mL, ℃, and psi.
Constraints on Multi-turn Conversation and Prompts
The rigor and standardized structure of small molecule pharmaceutical regulatory documents require multi-turn conversation systems to precisely identify terminology and context in user queries. For example, queries about batch numbers or expiry dates require the system to accurately extract corresponding values from documents and explain them with units. In multi-turn conversations, users may ask follow-up questions about specific procedural details or the basis for certain quality standards. This requires the system to recall relevant clauses and maintain focus on the topic in subsequent dialogue. The periodic updates to documents mean the knowledge base needs regular synchronization to ensure timely and accurate answers. Furthermore, due to specialized terminology and units of measurement, prompt design must guide the model to focus on this critical information, avoiding vague responses or misinterpretations.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8 | Ensures context covers key information in multi-turn conversations, preventing loss of prior context. |
Chunk size (Segment Length) | 500 characters (characters) | Balances text block completeness with recall efficiency, suitable for the paragraph structure of regulatory documents. |
Recall count (Recall Count) | Top 5 entries (top 5) | Improves recall relevance and reduces interference from irrelevant information during model inference. |
Similarity threshold (Similarity Threshold) | 0.75 | Filters for highly relevant text segments, ensuring answer accuracy. |
Rerank result count (Rerank Return Count) | 3 | Further refines recall results, improves model processing efficiency, and focuses on core information. |
temperature | 0.1 | Reduces randomness in model responses, ensuring rigor and factual accuracy. |
Common Pitfalls
- Response format does not meet expectations, for example, failing to return a JSON Schema defined structure. This occurs when prompt constraints on output format are unclear or the model does not strictly follow instructions.
- After multi-turn conversations, the system "forgets" and cannot link to previous dialogue content. This may happen if
maxContextis set too low, leading to historical conversations being truncated. - When users ask about specific operating procedures, the system provides overly general responses or cites irrelevant clauses. This happens when the knowledge base segmentation strategy fails to retain the complete context of operating procedures, or the granularity of recall results is too broad.
Validation Steps
- Conduct multi-turn conversation tests using typical small molecule pharmaceutical regulatory queries. Check if responses are accurate, complete, and logically consistent.
- Randomly select key terms or operating procedures from regulatory documents. Verify the system's ability to accurately recall relevant clauses and explain them correctly.
- Simulate user questions that are vague or ambiguous. Observe whether the system can clarify intent through multi-turn questioning and ultimately provide effective answers.
The values provided are common starting points. Measure them against specific 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.