Multi-turn Conversations and Prompts for CRO Products

Data Characteristics in the CRO Sector

CRO (Contract Research Organization) product data primarily originates from experimental reports, clinical trial protocols, research agreements, compound structure information, biological sample bank records, instrument analysis results, and project management documents. This data typically exists as unstructured text (e.g., PDF experimental reports, Word protocols), semi-structured data (e.g., CSV or Excel files exported from LIMS systems), and structured data (e.g., compound IDs, batch information, detection indicators, and values in databases). Data update frequency varies by project stage, ranging from weekly updates in preclinical research to daily or real-time updates during clinical trials. Document structures are highly standardized, adhering to industry regulations like GLP/GCP, and include clear section headings, figures, and references. Fields and units are highly specialized, for example, "IC50 value (nM)," "PK parameters (AUC, Cmax)," "cell viability (%)"; precision and unit consistency are critical.

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

The high specialization and standardized document structure of CRO product data require multi-turn conversational systems to accurately understand specialized terminology and contextual nuances. The prevalence of unstructured data necessitates robust text parsing capabilities to efficiently extract key information from reports. For instance, when a user queries toxicity data for a compound, the system must aggregate relevant IC50 values and dose-response curves from multiple experimental reports. Varying data update frequencies challenge the real-time nature of the knowledge base; the system must ensure that data referenced in multi-turn conversations is the latest version. The rigor of specialized fields and units demands that prompt design include clear unit and data type constraints to prevent "garbled" output or result deviations due to unit confusion. In multi-turn conversations, users may progressively refine query conditions, for example, first asking about "in vitro efficacy of Compound A," then adding "performance in different cell lines." This requires the system to remember and integrate prior conversational information.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale for Recommendation
`maxContext`	`6` turns	Balances memory capability with avoiding performance degradation from excessively long contexts, suitable for most CRO query scenarios.
`Chunk size` (Segment Length)	`800` characters	CRO documents are highly specialized with large amounts of information per segment; this length helps retain complete semantic meaning.
`Recall count` (Recall Count)	`8` items	Increases the probability of recalling relevant documents, covering a broader range of experimental data and reports.
`Similarity threshold` (Similarity Threshold)	`0.75`	Ensures the precision of recalled content, preventing irrelevant or low-relevance experimental reports from being included.
`Rerank result count` (Reranked Return Count)	`3` items	Prioritizes displaying the most relevant, core experimental conclusions or key indicator data.
`Prompt Template` (Prompt Template)	Includes "`Please UseCROExperimental Report Format Summary`" (Please summarize in CRO experimental report format)	Guides the model to output structured answers compliant with industry standards, facilitating quick understanding by engineers.

Three Common Pitfalls

The model fails to remember previous questions in multi-turn conversations, leading to answers disconnected from the context. This occurs when the maxContext parameter is set too low, or the knowledge base segmentation strategy truncates critical information, preventing it from being effectively passed to the model.
Key information extracted from user conversations appears garbled or in an incorrect format. This typically results from prompt design failing to explicitly constrain output format or encoding, causing the model's output to not meet expectations, especially when handling specific biochemical symbols or units.
The system takes too long to respond when handling complex compound structures or experimental procedure queries. This may be due to a Chunk size (segment length) that is too small in the knowledge base index, leading to an excessive recall of fragmented information, or a Recall count (recall count) that is too large, increasing the processing burden during the RAG (Retrieval Augmented Generation) phase.

How to Verify Correct Configuration

Conduct multi-turn conversation tests to verify whether the model can accurately understand and reference specialized terminology and key information from previous turns in continuous questioning.
Simulate user queries for typical CRO experimental reports (e.g., pharmacodynamics reports, toxicology reports), checking if the system's output accurately extracts key values like IC50, LD50, and ensures correct units.
Test with CRO documents in various file formats (PDF, CSV, Word) to confirm the system can stably parse and extract valid information from different document structures, with no garbled output.
Perform stress tests to observe the system's response speed and stability for multi-turn conversations under concurrent queries, ensuring performance meets requirements within the expected maxContext range.

Note: 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.