Data Characteristics for This Category
Lead optimization regulation data primarily comes from internal enterprise documents such as R&D specifications, experimental records, compliance files, and project management manuals. These documents are typically stored in PDF, Word, or Markdown formats. Content covers compound synthesis processes, screening criteria, toxicology assessment guidelines, patent application procedures, and internal approval systems. The update frequency is low, usually occurring with major adjustments to R&D pipelines or regulatory updates, possibly only once every few months or even a year. Document structures typically include chapters, sections, figures, and references. Fields and units strictly adhere to professional norms in chemistry, biology, and pharmacology, such as compound structural formulas, IC50 values (nM), PK parameters (e.g., Cmax ng/mL, T1/2 h), and safety indicators.
Constraints Imposed by These Characteristics on Multiturn Conversations and Prompts
The specialized nature and low update frequency of lead optimization regulation documents require the multiturn conversation system to precisely identify professional terminology and context when understanding user queries. For example, when a user mentions "IC50," the system needs to recognize it as an activity indicator and link it to relevant compound screening criteria. Documents contain numerous figures and structural formulas, which limits the effectiveness of pure text retrieval. This necessitates stronger multimodal processing or deeper understanding of descriptive text content. The low update frequency means high knowledge base stability but demands historical version traceability. The system may need to answer questions like "What was the XX standard in the 2023 version of the regulation?" Additionally, strict compliance requirements mean the system must avoid speculation or generalization when generating answers. All responses must be directly or indirectly traceable to the original text to ensure information accuracy and authority.
Configuration Settings
| Configuration Item | Suggested Value | Rationale for This Value |
|---|---|---|
Chunk size | 500–800 characters | Balances the completeness of professional terminology with retrieval efficiency, avoiding the introduction of excessive irrelevant information in long paragraphs. |
Recall count | Top 8–12 entries | Ensures coverage of multiple information points, addressing potential connections between different sections of regulations. |
Similarity threshold | 0.78–0.85 | Balances retrieval precision and recall rate, reducing misjudgment of professional terminology while capturing semantically similar but differently worded paragraphs. |
Rerank result count | Top 5 entries | Optimizes response speed for multiturn conversations, focusing on the most relevant information and reducing the model's processing burden. |
Prompt Max Length | 3000 characters | Accommodates complex queries and the accumulation of context in multiturn conversations, ensuring all necessary information is received by the model. |
Model Temperature (temperature) | 0.1–0.3 | Reduces model divergence, ensuring the rigor and traceability of answers, and preventing the generation of inaccurate or speculative content. |
Three Common Mistakes
- Phenomenon: The system fails to recognize user-mentioned acronyms like "ADMET," leading to a failure to retrieve relevant content. Reason: The knowledge base did not effectively process or expand professional acronyms during chunking or embedding, resulting in a mismatch with the original text.
- Phenomenon: When a user asks about regulation revisions for "this month" or "this year," the system provides an empty or vague answer. Reason: The prompt did not effectively guide the model to use external time information, or the knowledge base lacked timestamp metadata, preventing association with regulation versions within specific time periods.
- Phenomenon: Markdown formatted content output by the model is not rendered and appears directly as raw Markdown text. Reason: The frontend display component is not correctly configured or Markdown rendering functionality is not enabled, leading to raw text output.
How to Confirm Correct Configuration
- For a batch of test questions containing professional terminology and acronyms, verify whether the retrieval results include all relevant regulation clauses. Check if the retrieved items precisely match the query intent, and ensure their similarity scores are above the set threshold.
- Construct test cases covering multiturn conversation scenarios. During the conversation, check if the model correctly understands the context and provides reasonable inferences and answers based on previous dialogue content, especially for questions involving cross-chapter or multi-document associations.
- Verify whether the system can accurately filter regulation versions within the corresponding time range when handling queries with time constraints (e.g., "regulations published last year"), and answer based on their content, confirming its version traceability.
- Check if the model's output answers are traceable, meaning every key information point can be found in the original knowledge base text, and the output format (e.g., Markdown rendering) meets expectations.
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.