Multi-turn Conversation and Prompts for Recombinant Protein

Data Characteristics for This Category

Recombinant protein pharmacovigilance data originates primarily from clinical trial reports, real-world studies, post-market surveillance, and public databases from global drug regulatory agencies (e.g., FDA Adverse Event Reporting System, FAERS; European Medicines Agency EudraVigilance). Data updates are frequent, typically on a quarterly or monthly rolling basis. Document structures are predominantly semi-structured and unstructured, including Case Report Forms (CRFs), medical literature abstracts, and patient interview records. Key fields include patient demographics, medication history, adverse event (AE) descriptions (e.g., severity, onset_date), causality_assessment, batch_number, specific modification_type of the recombinant protein, and expression_system. Adverse event descriptions often contain a mix of medical terminology and natural language. Units involve dosage (mg, IU), frequency (times/day), and duration (days, weeks).

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

Recombinant protein data contains extensive specialized medical terminology and abbreviations. This requires multi-turn conversation systems to possess a high degree of semantic understanding, accurately parsing complex user queries. For example, structural differences between recombinant proteins can lead to specific immunogenicity. Frequent data updates mean knowledge bases must maintain high timeliness, avoiding the use of outdated information that could lead to misjudgments. The semi-structured and unstructured nature of documents makes extracting key adverse event information from case reports challenging. Prompts must guide the model to focus on specific fields. Recombinant protein manufacturing processes and batch differences can affect safety. Multi-turn conversations need to trace back to specific production information. Patient variability and concomitant medication complicate causality assessment. Prompt design must guide the model to consider multi-dimensional information, avoiding conclusions based on a single clue.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`8`	Ensures sufficient historical information is retained in multi-turn conversations, covering common symptom inquiries and medication history verification.
`Chunk size` (Chunk Length)	`500–800 characters` (characters)	Accommodates the length of adverse event descriptions in case reports, balancing semantic completeness with retrieval efficiency.
`Recall count` (Recall Count)	`Top 8–12 entries` (top 8–12 items)	Given the complexity of recombinant protein adverse reactions, more relevant documents need to be recalled for comprehensive model judgment.
`Similarity threshold` (Similarity Threshold)	`0.75`	Addresses the precise matching requirements for medical terminology, increasing the similarity threshold to reduce interference from irrelevant information.
`Rerank result count` (Reranked Return Count)	`5`	Based on a high recall volume, reranking refines the most relevant items, improving the accuracy of the final response.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Handles the parsing time for large clinical trial reports or complex case documents, preventing processing failures due to timeouts.

Three Common Pitfalls

The conversation states, "Unable to find adverse event information related to this batch number." This may be because the knowledge base was not updated promptly, or the batch_number field was not correctly extracted during document parsing.
The model provides overly general or ambiguous conclusions when answering questions about the causality of recombinant protein adverse reactions. This occurs because prompts fail to effectively guide the model to focus on the causality_assessment field and analyze it in conjunction with the patient's medication history.
A user queries about a specific recombinant protein modification, but the system returns general adverse reaction information. The returned results lack modification_type-related details. This may be because the knowledge base chunking strategy is too coarse, failing to retain sufficiently granular modification information.

How to Confirm Correct Configuration

Input complex queries containing specialized medical terminology. Observe if the system accurately identifies and returns relevant adverse event reports. Verify if the severity and onset_date in the returned reports match the query intent.
Engage in multi-turn conversations, simulating inquiries about patient medication history and specific symptoms. Check if the system can consistently provide coherent and accurate recombinant protein adverse reaction information based on historical conversation context.
Upload a new clinical trial report. After system parsing is complete, use keyword queries to confirm if the expression_system and modification_type fields from the report are retrievable and cited.
Perform multi-batch adverse event queries against recently updated FAERS data. Verify if the results include the latest recombinant protein-related reports and check the timeliness of the update_time field.

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.