Multi-turn Conversations and Prompts for Preclinical Safety

Data Characteristics for This Category

Preclinical safety assessment quality documents primarily include GLP (Good Laboratory Practice) compliance files, SOPs (Standard Operating Procedures), study protocols, raw data records, study reports, and related appendices. These documents typically use formats such as PDF, Word, and Excel. Data update frequency is relatively low, mainly occurring when submitting phased new drug development reports, updating regulations, or optimizing internal processes. Document structures are highly standardized. For example, GLP guidelines follow strict chapter numbering and appendix formats. Study reports contain fixed sections like abstracts, introductions, materials and methods, results, and discussions. Fields and units are highly specialized. For instance, dosage is often expressed in mg/kg, concentration in μg/mL, time in h or d, and pathological descriptions include organ names and lesion severity. Data sources are mainly internal R&D teams, CROs (Contract Research Organizations), and regulatory guidance.

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

The specialized and standardized structure of preclinical safety assessment documents requires multi-turn dialogue systems to accurately understand domain-specific terminology. The system must also extract information based on specific chapters or fields within the documents. Low update frequency means that after knowledge base construction, the focus should be on retrieval efficiency and accuracy, without frequent full knowledge updates. The large volume of structured text and fixed formats makes document parsing and chunking strategies critical to quickly locate relevant information in multi-turn conversations. The presence of specialized fields and units necessitates prompt design that guides the model to focus on this key information. This avoids incorrect or vague answers due to the model's misunderstanding of professional terms. Additionally, due to high information security requirements, the dialogue system must effectively distinguish and handle sensitive information, ensuring no information leakage during multi-turn interactions.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`8`	In preclinical safety assessment multi-turn conversations, users often need to trace back definitions of professional terms or data from previous turns. 8 turns cover most scenarios.
`Chunk Length`	`800 characters`	Preclinical safety assessment document paragraphs are often long, containing detailed descriptions and data. 800 characters maintain semantic integrity.
`Recall Count`	`Top 5`	Ensures the recall of core paragraphs highly relevant to the query, avoiding the introduction of excessive irrelevant information that could affect model judgment.
`Similarity Threshold`	`0.75`	The preclinical safety assessment field demands high information accuracy. A threshold of 0.75 effectively filters out low-relevance results.
`Rerank Return Count`	`3`	Reranks the recalled results, ensuring the top 3 most relevant pieces of information are presented first to the large language model.
`Prompt Template`	Includes `"Please strictly answer the question based on the provided preclinical safety assessment document content, and indicate the cited chapter or page number."`	Emphasizes information source and professionalism, guiding the model to give precise answers and provide traceability.

Three Common Mistakes

An Unexpected end of JSON input error in a conversation typically occurs when the backend service receives an incomplete or malformed JSON string while processing the large language model's response.
The model fails to maintain contextual consistency in multi-turn conversations, appearing disconnected from previous questions. This is because the maxContext parameter is set too low, leading to truncation of historical conversation information.
The AI frequently uses Markdown syntax in its answers, even when the question does not require formatted output. This happens because the prompt does not explicitly instruct on the output format, or the model defaults to using Markdown.

How to Verify Configuration

Select a GLP compliance document containing complex tables and multi-level headings. Conduct multi-turn questioning to check if the AI's answers accurately extract table data and link it to corresponding headings.
Ask follow-up questions about specific toxicology indicators (e.g., LD50 or NOAEL values). Verify if the AI can precisely identify and cite these values with units from the document.
Simulate a user gradually refining a question. For example, from "Please describe the safety of a certain drug" to "Please detail the cardiac toxicity study results of this drug in dogs." Confirm if the AI can focus on more specific document content during multi-turn interaction.

The values given 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.