Multi-Turn Conversations and Prompts for Recombinant Protein

Data Characteristics

Recombinant protein R&D documents originate from lab notebooks, experiment reports, batch production records, quality control reports, and preclinical study reports. These documents update frequently, especially during early R&D stages, with frequent iterations of experimental data and results. Document structures typically include clear titles, sections, figures, tables, and attachments. Key information includes batch numbers, expression hosts, purification methods, yields, purity, activity units, molecular weight, and isoelectric points. Fields and units are highly specialized, for example, "ug/mL" for concentration, "U/mg" for specific activity, and "kDa" for molecular weight. Documents can exist in various formats, including PDF, Word, and Excel, with some data in unstructured text.

Constraints on Multi-Turn Conversations and Prompts

The specialized and structured nature of recombinant protein R&D documents imposes specific requirements on multi-turn conversation and prompt design. First, specialized terminology and abbreviations require prompts to accurately identify and link to definitions in the knowledge base, preventing semantic drift. Second, multi-turn conversations must support tracing and comparing data for specific batches or experimental conditions. This requires prompts to guide users in defining query scope and extracting precise values from complex tables and figures. For example, when querying "purity of a certain protein batch," the system must understand the mapping between "batch" and "purity" and the corresponding fields in the document. Finally, frequent document updates challenge knowledge base real-time capabilities. The conversation system must handle coexisting old and new data and prompt users with data timestamps or version information.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`600–800 characters`	Recombinant protein documents are information-dense. Shorter contexts may lose critical data, while longer contexts increase computational load.
`Chunk size` (Segment Length)	`300–400 characters`	Ensures each segment contains at least one complete experimental result description or critical parameter set for effective retrieval.
`Recall count` (Retrieval Count)	`Top 5–8`	Considering the complexity of recombinant protein R&D, multiple relevant segments help provide comprehensive information and reduce omissions.
`Similarity threshold` (Similarity Threshold)	`0.75–0.85`	A higher threshold ensures the professionalism and relevance of retrieved content, avoiding the introduction of irrelevant biological background knowledge.
`Rerank result count` (Reranked Return Count)	`Top 3`	Further refines retrieved results, prioritizing the most direct and critical experimental data and conclusions.
`Answer Length Limit`	`200–300 words`	Ensures answers provide sufficient information without being overly verbose, allowing engineers to quickly obtain core data.

Common Pitfalls

The conversation shows "Purity data not found," but logs indicate FIELD_NOT_FOUND. This occurs when the prompt does not explicitly specify the purity type (e.g., SDS-PAGE purity, HPLC purity) or when field names are inconsistent in the knowledge base.
After a user query, there is a long delay followed by a TIMEOUT_ERROR. This can happen when queries involve real-time parsing of many figures or large PDF documents, consuming significant computational resources without sufficient timeout configuration.
The answer cites irrelevant experimental results or batch information. This occurs when prompts in multi-turn conversations fail to effectively guide the user to clarify the query scope, leading to the retrieval of semantically similar data that does not belong to the target batch or experiment.

Verification Steps

For typical queries (e.g., "What is the specific activity of recombinant protein BP20230501?"), verify that the answer accurately extracts and presents the corresponding numerical value and unit, and check that the cited document snippets are precise.
Simulate a series of multi-turn conversations, for example, first querying "BP20230501's purity," then asking about "yield for the same batch." Confirm the system maintains conversational context and accurately links information.
Update the knowledge base with a new experiment report. Immediately perform relevant queries to confirm the system promptly indexes and uses the latest data, and can provide data timestamps or version information.

The values provided are common starting points. Measure them 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.