Multi-turn Conversations and Prompts for Recombinant Protein

Data Characteristics

Recombinant protein data primarily comes from datasheets, technical documents, batch release reports, and research papers published by biotechnology companies. These documents are typically in PDF, DOCX, or XLSX formats. Data update frequency is relatively stable, usually occurring when product batches are updated or new products are launched. Datasheets typically contain standard fields such as product name, catalog number, batch number, concentration, purity, activity units, storage conditions, and application recommendations. Batch reports detail test results for each batch, such as SDS-PAGE purity percentage, endotoxin content (EU/mg), and biological activity data (e.g., EC50 value). Concentration units are commonly mg/mL or μg/mL. Activity units vary depending on the specific protein function, such as IU/mg, units/ug, or EC50 values.

Constraints from "Multi-turn Conversations and Prompts"

The coexistence of structured and semi-structured data in recombinant protein documents demands accuracy in multi-turn conversations. For example, when a user queries the purity of a specific batch, the model must accurately identify tabular data within the document. The diversity of activity units (e.g., IU/mg, units/ug, EC50) requires prompt design to guide the model in understanding unit context, avoiding confusion. Numerical data in batch reports (e.g., endotoxin content, EC50 values) may involve comparison or calculation in multi-turn conversations, requiring the model to have some numerical reasoning capability. Additionally, product application recommendations are often descriptive text, requiring the model to extract key information from unstructured text and summarize it based on user questions. Data update frequency determines the knowledge base refresh cycle, ensuring model responses are based on the latest product information.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Segment Length`	`400–600 characters`	Balances short descriptions in recombinant protein datasheets and table rows in batch reports, ensuring semantic completeness.
`Recall Count`	`Top 8`	Ensures coverage of potential relevant information from different sources (datasheets, batch reports), reducing omissions.
`Similarity Threshold`	`0.75–0.8`	Considers the specificity of recombinant protein names and parameter descriptions, improving recall accuracy.
`Rerank Return Count`	`Top 4`	Further filters the most relevant and information-dense segments from the initial recall, avoiding interference from irrelevant information.
`Max History Messages`	`6`	Balances the contextual coherence of multi-turn conversations with the computational cost of model complexity, covering common follow-up scenarios.
`prompt_template`	Includes "Please focus on recombinant protein name, catalog number, batch number, concentration, purity, activity, and storage conditions."	Explicitly guides the model to prioritize extracting and organizing key attributes of recombinant proteins when generating responses.

Common Pitfalls

The conversation displays "No relevant batch activity data found," but the data exists in the document: This may be due to inaccurate batch number identification or diverse expression forms of activity data, which the model failed to extract correctly.
A user asks to compare concentrations of different recombinant proteins, and the model provides incorrect comparison results: This usually happens when the model fails to correctly identify units or convert units, directly comparing numerical values.
After uploading an XLSX batch report, some key data (e.g., endotoxin content) cannot be referenced by the AI: This may be because the table structure or specific cell formats were not correctly recognized during XLSX file parsing, leading to data extraction failure.

Validation Steps

Upload a set of typical recombinant protein datasheets and batch reports. Test whether multi-turn conversations can accurately answer questions about product concentration, purity, activity values, and storage conditions.
For different recombinant protein batches, ask comparative questions about specific test indicators (e.g., endotoxin) and check if the model provides accurate values and units.
Test the model's ability to understand and summarize product application recommendations. For example, ask "What experiments can this protein be used for?" and check if the response covers the main application directions in the document.
Randomly select key fields from the document (e.g., EC50 value) and ask about their meaning or value in a conversation, verifying if the model can explain them correctly.

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.