Multi-Turn Conversations and Prompts for On-Call Transfer in

Data Characteristics for This Category

Data for on-call transfers in WeChat Work groups within the biopharmaceutical industry primarily comes from internal on-call scheduling systems, historical patient or customer consultation records, and drug inserts or medical device operating manuals. Scheduling system data updates weekly or monthly and includes fields such as on-call personnel name, department, specialty, contact information, and on-call time slots. Historical consultation records are unstructured text, containing patient chief complaints, disease diagnoses, medication use, and follow-up results. Drug inserts and device manuals are relatively static documents with longer update cycles, but they contain highly specialized content, covering drug ingredients, indications, contraindications, dosage, adverse reactions, as well as device parameters, operating procedures, and precautions. Together, these data form the foundational information required for on-call transfer conversations.

Constraints Imposed by These Characteristics on Multi-Turn Conversations and Prompts

The coexistence of structured and unstructured on-call transfer data presents specific requirements for multi-turn conversation prompt design. Structured data from scheduling systems requires precise matching, for example, querying by on-call personnel name and on-call time slot. Unstructured historical consultation records and manuals demand strong semantic understanding from prompts to extract key information from complex text and determine the relevance of consultation content to the knowledge base. Due to the numerous and rigorous specialized terms in the biopharmaceutical field, prompts must guide the model to accurately identify medical vocabulary and avoid ambiguity. Furthermore, the timeliness of on-call information requires dynamic updates and references to the latest scheduling data during conversations, while ensuring stable retrieval of static knowledge like drug inserts.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`maxContext`	`8`	Ensures enough context is retained in multi-turn conversations to cover initial patient descriptions, AI questions, and user supplementary information, enabling accurate on-call personnel matching or knowledge retrieval.
`Chunk size` (Segment Length)	`500 characters` (500 characters)	Balances semantic completeness and retrieval efficiency for drug inserts and historical consultation records, preventing overly long segments from diluting key information and overly short segments from losing context.
`Recall count` (Recall Count)	`Top 5 entries` (Top 5 items)	Provides multiple potential results for on-call personnel matching and knowledge base retrieval, increasing the success rate and offering more choices to the model.
`Similarity threshold` (Similarity Threshold)	Calibrate by actual measurement	Requires adjustment through actual testing based on the similarity of biopharmaceutical terminology to ensure highly relevant content is recalled while filtering out low-relevance information.
`Rerank result count` (Rerank Return Count)	`Top 3 entries` (Top 3 items)	Optimizes results further after recalling multiple items by reranking, prioritizing the most relevant on-call personnel or knowledge entries to improve transfer efficiency.
`system_prompt`	Includes a list of `specialized terms`	Guides the model to recognize and understand specialized vocabulary in the biopharmaceutical field, such as drug names, disease diagnoses, and examination items, improving conversation accuracy.

Three Common Pitfalls

The model fails to accurately identify disease or drug names described by the patient during the conversation, leading to irrelevant knowledge base retrieval results. This occurs because prompts do not sufficiently guide the model to focus on specific domain keyword recognition.
In multi-turn conversations, the model cannot remember previously mentioned on-call requirements or symptoms, resulting in repeated questions or incorrect transfers. This is due to the maxContext parameter being set too low, causing historical conversation information to be lost.
During on-call transfers, the system indicates that the corresponding on-call personnel cannot be found, even though they are present in the actual schedule. This typically results from delays in synchronizing scheduling data with the knowledge base or inaccurate field mapping, preventing the retrieval of the latest or correct structured information.

How to Verify Configuration

Simulate various on-call transfer scenarios, including emergencies, specific department consultations, and drug inquiries, to check if the model accurately identifies intent and provides appropriate transfer suggestions or knowledge links.
Compare the on-call personnel information provided by the model with actual data in the current scheduling system to verify the matching accuracy of key fields such as on-call personnel name, department, and on-call time slot.
Test the model with specialized terms from patient consultations to check if it correctly understands and recalls relevant content from drug inserts or historical consultation records. This can be assessed by observing the actual effects of Recall count (recall count) and Similarity threshold (similarity threshold).
Evaluate the coherence of multi-turn conversations to ensure that the model retains memory of the user's core needs after multiple interactions, avoiding repetitive questioning. This can be confirmed by observing the practical effect of maxContext.

The values given 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.