Data Characteristics
Preclinical safety assessment data originates from pharmacology and toxicology research reports, GLP (Good Laboratory Practice) laboratory raw records, and CRO (Contract Research Organization) summary reports. These documents have a relatively low update frequency, typically archived at project milestones. The document structure is highly standardized, adhering to guidelines such as ICH (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use) M3(R2). It includes clear section titles like "Study Design," "Test Article Information," "Animal Grouping," "Dosing Route and Dose," "Observation Indicators and Results," and "Statistical Analysis." Field specificity is strong, involving precise units and biological significance for terms such as "dosing dose (mg/kg)," "dosing frequency (times/day)," "plasma concentration (ng/mL)," "organ coefficient (%)," and "pathological diagnosis."
Constraints Imposed by These Characteristics on Multi-turn Conversations and Prompts
The standardization and specialized nature of preclinical safety assessment data demand high accuracy in understanding and generation from multi-turn dialogue systems. The high degree of document structure allows for prompt design to guide the model more precisely in extracting specific sections or table information. For example, a query about "toxic dose" can be explicitly limited to the "dose-response relationship" section. The strictness of fields and units means the model must accurately identify and extract numerical values and their corresponding units, avoiding misinterpretations due to unit confusion. The low update frequency permits more in-depth preprocessing and indexing optimization during knowledge base construction, reducing the complexity of real-time updates. Simultaneously, the density of specialized terminology requires the model to have strong domain vocabulary understanding, effectively handling out-of-vocabulary terms or abbreviations, which directly impacts the accurate expression of specialized terms in prompts.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8 | Preclinical safety assessment reports have strong content correlation; maintaining appropriate context helps understand referential relationships in multi-turn queries. |
Chunk size (Segment Length) | 800–1200 characters | Ensures a single segment can contain a complete experimental method description or result table, preventing semantic fragmentation. |
Recall count (Number of Retrieved Items) | Top 5 entries (Top 5) | Preclinical safety assessment queries typically require detailed and highly relevant information; increasing retrieval quantity improves information coverage. |
Similarity threshold (Similarity Threshold) | 0.78–0.85 | This range effectively filters out irrelevant passages, addressing the precise matching requirements for specialized terms and numerical values. |
Rerank result count (Number of Reranked Items) | 3 | Selects a small number of the most relevant pieces of information for display, reducing the engineer's reading burden and focusing on core results. |
temperature | 0.3–0.5 | Ensures objective and accurate model output, avoiding creative bias in factual queries. |
Three Common Pitfalls
- Issue: During a conversation, the model fails to recognize abbreviations like "LD50" or "NOAEL," leading to query failure. Reason: The knowledge base construction did not effectively identify and expand professional abbreviations, and the prompt did not include corresponding explanations or examples.
- Issue: When a user asks about "dosing dose," the model returns a numerical value without units or with incorrect units. Reason: The text extraction stage failed to correctly associate numerical values with units, or the prompt did not explicitly require the model to return values with units.
- Issue: In a continuous conversation, the model forgets the previous round's discussion about a specific compound and requires re-specification. Reason: The
maxContextparameter was set too low, causing the conversation history to be truncated and unable to maintain long-term context.
How to Confirm Proper Configuration
- Conduct a series of test queries containing specialized terms, abbreviations, and numerical units to check if the model can accurately understand and extract information.
- Design multi-turn dialogue scenarios to verify if the model can maintain contextual memory and correctly handle referential relationships and progressive questioning.
- Compare the experimental results extracted by the model with the data in the original documents, checking the consistency of numerical values and units to determine if extraction accuracy meets expectations.
- Simulate engineers' actual querying habits, test questions of varying complexity, evaluate system response speed and result relevance, and adjust the
Similarity threshold(Similarity Threshold) based on feedback.
Note: The values provided are common starting points and should be measured against your 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.