Multiturn Conversations and Prompts for Preclinical Safety

Data Characteristics for This Category

Preclinical safety assessment product data primarily comes from research reports, experimental records, regulatory documents, and scientific literature. This data updates infrequently, typically with project progress or regulatory revisions. Document structures are mostly unstructured text, such as PDF research reports, Word document experimental protocols, and scanned image data. These documents contain extensive specialized terminology, chemical structures, dosage units (e.g., mg/kg, µg/mL), time units (e.g., hours, days, weeks), and statistical symbols. The data is highly specialized, information-dense, and often involves multiple experimental batches and control group data, requiring precise identification of subtle differences between batches.

Constraints Imposed by These Characteristics on Multiturn Conversations and Prompts

The highly specialized and unstructured nature of preclinical safety assessment data challenges the accuracy and depth of multiturn conversations. Due to the large number of specialized terms and symbols, the model requires strong semantic understanding to avoid ambiguity in user queries. Multi-batch experimental data and detailed measurement units in documents require the dialogue system to accurately extract and compare numerical values, such as identifying specific experimental data under GLP guidelines. The low update frequency means knowledge base construction must prioritize historical data completeness and version management. Additionally, common charts and tables in documents require prompt design to guide the model in extracting this non-textual information from text descriptions and integrating it into the dialogue context, ensuring comprehensive and accurate responses.

Configuration Settings

Configuration Item	Recommended Value	Rationale for Recommendation
`Chunk Size`	`500–800 characters`	Preclinical safety assessment documents are information-dense. Shorter chunks help maintain context coherence and prevent information loss.
`Recall Count`	`8–12 items`	Ensures coverage of multiple relevant experimental reports or regulatory clauses for complex queries.
`Similarity Threshold`	`0.75–0.85`	The domain requires high specificity. A threshold that is too low may introduce irrelevant content, while one that is too high may miss highly relevant documents.
`Rerank Return Count`	`5 items`	Reranks recall results to prioritize detailed experimental data that best matches user intent.
`maxContext`	`32000 tokens`	Preclinical safety assessment questions often have long contexts, including multiple experimental details and background information.
`queryRewrite`	Enabled	Rewrites potential professional abbreviations or non-standard expressions in user queries to improve recall accuracy.

Three Common Mistakes

Symptom: A user asks about the toxic dose of a compound, but the model provides general research methods. Cause: The prompt failed to explicitly guide the model to focus on numerical data extraction, or the knowledge base lacked dosage data for the specific compound.
Symptom: An API call returns 400 Bad Request with a message indicating an invalid customUid field. Cause: The customUid passed by the external system does not conform to the API interface specification, or historical records were not correctly filtered by customUid when retrieved.
Symptom: The conversation history contains many irrelevant dialogue segments. Cause: Conversation history management failed to isolate records by application and user with fine granularity, leading to mixed dialogue records between different users or applications.

How to Confirm Correct Configuration

For typical preclinical safety assessment queries (e.g., "What is the LD50 of compound XX?"), verify that the model's answer includes precise numerical values and units, and compare them with the original documents.
Simulate user queries to check if the model correctly understands and tracks context during multiturn conversations. For example, when asking "What about its NOAEL?", the model should relate it to the compound from the previous turn.
Use API calls with different customUid values to initiate conversations and query historical records, confirming that historical record isolation meets expectations.
Test the model's ability to recognize specialized terminology and measurement units, such as mg/kg and µg/mL, to check if the model can correctly parse and use them for retrieval.

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.