Multi-turn Conversation and Prompt Engineering for

Data Characteristics in this Category

Laboratory service data in biomedical R&D primarily comes from experiment reports, analysis records, methodology documents, and instrument operation manuals. This data typically exists in formats such as PDF, DOCX, and XLSX, characterized by high specialization and structured content. Update frequency is relatively stable, usually occurring during experiment project progression or methodology optimization. Document content includes numerous specialized terms, abbreviations, units of measurement (e.g., nM, µg/mL, %), and experimental parameters (e.g., pH value, temperature, reaction time). Reports often contain tables, charts, and images describing experimental steps, result data, and analysis conclusions. Data fields are highly domain-specific, such as IC50, EC50, Kd values, and various protein, nucleic acid, and cell line identifiers.

Constraints Imposed by These Characteristics on "Multi-turn Conversation and Prompt Engineering"

The specialized and structured nature of laboratory service documents imposes specific requirements on multi-turn conversation and prompt engineering. The large volume of domain-specific terms and abbreviations requires the model to accurately recognize entities and avoid semantic drift. The inclusion of tables, charts, and images necessitates efficient multimodal processing capabilities to ensure complete information extraction. Units of measurement and experimental parameters in the data require accurate numerical comparison and logical reasoning in conversations. For example, when querying "the activity of a certain drug at pH 7.4," the model needs to understand the meaning of pH and locate relevant data from the document. The document update frequency affects the knowledge base refresh strategy to ensure conversations are based on the latest information. Additionally, user queries often involve complex causal relationships and multi-conditional filtering, requiring prompt design to guide the model in deep semantic understanding and relational analysis.

Configuration Settings

Configuration Item	Recommended Value	Rationale for this Value
`Chunk size` (Chunk Size)	`500-800 characters` (characters)	Balances contextual relevance with the amount of information processed in a single pass, especially suitable for documents containing lengthy experimental procedure descriptions.
`Recall count` (Recall Count)	`Top 8-12 entries` (top 8-12 entries)	Considering the high knowledge density in R&D documents, increasing the recall count helps cover more potentially relevant segments.
`Similarity threshold` (Similarity Threshold)	`0.75-0.85`	For queries involving specialized terms and precise numerical values, a higher threshold ensures the precision of recalled results.
`Rerank result count` (Rerank Return Count)	`Top 3-5 entries` (top 3-5 entries)	Further filters out segments most relevant to the user's intent, improving the accuracy of the final response.
`maxContext`	`32000 token`	Ensures that multi-turn conversations can accommodate sufficient historical dialogue and recalled document content, supporting complex question tracing.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Provides ample file parsing time when processing large experiment reports or documents containing numerous charts.

Three Common Mistakes

Conversation results fail to output image or table data from documents. This is typically due to incorrect handling of multimodal content during knowledge base construction, where image links or table content are not effectively embedded or associated.
The model exhibits confusion or calculation errors when processing queries involving units of measurement, such as inability to distinguish between nM and µM. This occurs when prompts lack clear instructions for unit conversion and numerical comparison, or when units are not standardized in the knowledge base.
In multi-turn conversations, the model fails to accurately trace specific experimental conditions or compound names mentioned in previous turns, leading to context loss. This often happens when maxContext is set too low, unable to accommodate the complete conversation history and relevant knowledge segments.

How to Confirm Proper Configuration

Submit queries containing specialized terms and abbreviations to check if the model accurately identifies and provides relevant definitions or explanations. Verify that the results align with document content.
Construct documents containing table data and image descriptions. Ask the model to summarize table content or describe image information. Confirm whether the model's output includes this multimodal information and compare the accuracy of extraction.
Conduct multi-turn conversation tests. In the second or third turn, reference experimental parameters or compounds mentioned in a previous turn. Observe if the model correctly understands and responds based on context. Evaluate context maintenance capability.
Design queries involving numerical comparison and unit conversion, such as "Which compound has a lower IC50 at different pH values?" Verify if the model can perform logical reasoning and numerical processing. Check if the output values and units are correct.

The values provided are common starting points and should be measured against specific 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.