Multi-turn Conversations and Prompts for Preclinical Safety

Data Characteristics in this Category

Preclinical safety assessment data primarily originates from pharmacology and toxicology research reports, GLP (Good Laboratory Practice) raw laboratory records, specialized reports, and expert evaluation opinions. Data update frequency is relatively low, typically aggregated and archived at key drug development milestones, such as the conclusion of toxicology studies or completion of pharmacokinetic studies. Document structures are highly standardized, adhering to regulatory guidelines from agencies like the FDA, EMA, and NMPA. These documents include detailed experimental methods, results, statistical analyses, and conclusions. Fields cover dosage, administration routes, animal species, observation indicators (e.g., body weight, organ coefficients, blood biochemical parameters, histopathological descriptions) and their units (e.g., mg/kg, g, U/L, umol/L), and safety assessment grades. Data typically exists as PDF, Word documents, Excel spreadsheets, or specialized database files.

Constraints Imposed by these Characteristics on Multi-turn Conversations and Prompts

The standardized and specialized nature of preclinical safety assessment data requires multi-turn conversation systems to accurately understand technical terminology and context. Strict document structures make extracting content from specific sections or tables critical, for example, extracting liver pathology descriptions for a specified dose group from a toxicology report. Low data update frequency means the system must effectively identify and compare differences between versions when processing historical data, ensuring the citation of the latest or corrected information. The rigor of fields and units demands prompt design that guides the model to accurately identify numerical values and their corresponding units, avoiding confusion, such as distinguishing mg/kg from ug/kg. Furthermore, due to the large volume of data and highly sensitive information, there is a high demand for information traceability and verification within conversation turns. The system needs to provide evidence chains or links to original documents.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`maxContext`	`8000 tokens`	Preclinical safety assessment reports are information-dense; a long context window helps the model understand relationships across multiple pages.
`Chunk size` (Chunk Size)	`800 characters` (characters)	Balances semantic completeness and retrieval efficiency, preventing excessive truncation of critical information.
`Recall count` (Retrieval Count)	`10 entries` (items)	Ensures coverage of multiple related but not directly hit knowledge points for complex queries.
`Similarity threshold` (Similarity Threshold)	`0.78`	Preclinical safety assessment terminology is professional and rigorous; a high threshold helps exclude ambiguous matches and improves accuracy.
`Rerank result count` (Reranked Return Count)	`5 entries` (items)	Focuses on core information most relevant to the current multi-turn conversation intent, reducing irrelevant interference.
`temperature`	`0.3`	Preclinical safety assessment data requires factual accuracy and logical rigor; a low `temperature` reduces model divergence and improves answer stability.

Common Pitfalls

Symptom: The model fails to accurately identify dosage units in reports, leading to incorrect numerical answers. Reason: Prompts do not clearly instruct the model to focus on and differentiate standard expressions for various dosage units, or the chunking strategy separates units from values.
Symptom: After a user uploads a large PDF report, the conversation interface remains unresponsive for an extended period or returns a 504 Gateway Timeout error. Reason: The UPLOAD_FILE_MAX_SIZE or PARSE_FILE_TIMEOUT_SECONDS configuration is too small, failing to accommodate the typically large file sizes and complex parsing requirements of preclinical safety assessment reports.
Symptom: The model's answers to queries about specific toxicology indicators are incomplete, omitting some critical observations. Reason: The System prompt defines the model's role and responsibilities too broadly, failing to explicitly require the model to comprehensively list relevant indicators and observational details when answering safety assessment questions. Additionally, Recall count (Retrieval Count) might be set too low to cover all relevant information.

How to Verify Configuration

Upload a typical toxicology report containing various dosage units. Ask about toxic reactions at specific dosages and verify the accuracy of dosage values and units in the model's response.
Select a structurally complex preclinical safety assessment report (e.g., containing multiple chapters, tables, appendices). Test whether the model can accurately extract summaries of different sections or specific table data, and check if maxContext is sufficient to handle such queries.
For a report containing revision history or different versions, ask questions about data changes or the latest conclusions. Verify if the model can identify the most recent information and provide relevant evidence sources.

Note: The values provided are common starting points. Measure them against your own samples to determine the optimal configuration.

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.