Multiturn Conversation and Prompts for Bispecific Antibody

Data Characteristics

Bispecific antibody (BsAb) regulations and Standard Operating Procedure (SOP) data primarily originate from guidelines issued by drug regulatory agencies, internal R&D and production quality management system documents, clinical trial protocols, and pharmacovigilance reports. These documents typically exist as PDFs, Word files, or internal knowledge base pages. Data update frequency correlates with regulatory policy releases, new drug development progress, and production process optimizations, usually on a quarterly or annual basis, with some critical guidelines released ad hoc. Document structures are often chapter-based, including standardized modules like introduction, definitions, responsibilities, operating procedures, and record-keeping requirements. Fields and units are highly specialized, for example, dosage units mg/kg, concentration units mg/mL, time units h, day, and specific quality control indicators such as purity (%), aggregate content (%).

Constraints Imposed by These Characteristics on "Multiturn Conversation and Prompts"

The specialized nature and standardized structure of bispecific antibody regulatory documents require multiturn conversation systems to possess precise semantic understanding to differentiate similar concepts. The periodic updates of documents mean the knowledge base needs regular synchronization with the latest versions to ensure timeliness and compliance of Q&A. Complex fields and units necessitate prompt design that explicitly instructs the model to focus on the correspondence between values and units, avoiding confusion or misinterpretation. For instance, when querying dosage, the system should distinguish between initial dose and maintenance dose and accurately extract values with mg/kg units. Furthermore, common procedural descriptions in documents, such as "if A occurs, then execute B; otherwise, execute C," demand higher logical branching and conditional judgment capabilities from multiturn conversations. Prompts must guide the model to identify and follow these business logic.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`Chunk size` (Segment Length)	`500–800 characters`	Regulatory documents have strict logic; overly short segments can break complete logic, while overly long ones add irrelevant information.
`Recall count` (Recall Count)	`Top 8–12 entries`	Ensures coverage of multiple relevant regulatory sections potentially involved in multiturn conversations, improving context completeness.
`Similarity threshold` (Similarity Threshold)	`0.75–0.85`	Bispecific antibody terminology is highly specialized; high similarity is required to avoid recalling irrelevant clauses.
`maxContext`	`3000–4000 tokens`	Regulatory Q&A often requires tracing multiple rounds of historical conversations; ensuring sufficient context length supports complex logic.
`Rerank result count` (Reranked Return Count)	`Top 3–5 entries`	Precisely orders the most relevant regulatory clauses, reducing the model's burden of processing irrelevant information and focusing on core answers.
`Prompt Temperature`	`0.3–0.5`	Regulatory Q&A prioritizes accuracy and rigor; a lower temperature reduces model's free-form generation, improving factual accuracy.

Three Common Mistakes

Symptom: After a user query, the system returns "workflow execution failed," and logs show workflow_execution_error. Cause: Parameters for calling external tools in the workflow do not match specific fields in bispecific antibody regulations, leading to abnormal tool invocation.
Symptom: In a multiturn conversation, the system fails to correctly associate follow-up questions about the same concept with the context from the previous turn. Cause: maxContext is set too small, leading to truncation of historical conversation information and the model losing critical contextual links.
Symptom: When asked about specific values in regulations (e.g., purity > 95%), the model fails to extract the specific value or unit. Cause: The prompt does not explicitly instruct the model to extract values and units, or the knowledge base segmentation separates values from units.

How to Confirm Proper Configuration

Select key clauses from bispecific antibody regulations and design test cases with multiple follow-up questions to verify if the system can accurately answer and maintain contextual coherence.
For descriptions in regulations that include conditional judgments (e.g., "if A then B"), test whether the system can correctly identify and execute the corresponding logical branches, outputting expected results.
Check if the system can accurately extract and reiterate professional terms, values, and units in queries, verifying the effectiveness of knowledge base segmentation and recall.
Simulate a user re-querying after a longer interval to observe if the system can continue a meaningful contextual conversation based on historical dialogue records, confirming maxContext is appropriately configured.

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