Multi-turn Conversation and Prompts for WeChat Work Group

Data Characteristics for This Category

Data for in-group Q&A in the biomedical field originates from daily member interactions, technical inquiries, product feedback, and internal knowledge sharing. This data updates frequently, often in real-time or near real-time. The document structure consists of unstructured chat logs containing numerous specialized terms, drug names, disease descriptions, and experimental data. Beyond standard sender, timestamp, and message content fields, specific drug batch numbers, experimental parameters, and patient symptom descriptions may be present, often scattered within the text without a unified format. The data is characterized by colloquial language, fragmentation, and often includes attachments like images and files.

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

The high real-time nature of in-group Q&A data requires multi-turn conversation systems to respond quickly and update knowledge bases promptly to handle the latest information. Unstructured chat logs and the presence of specialized terms and drug batch numbers make traditional keyword-based retrieval ineffective. This necessitates more sophisticated semantic understanding and entity recognition capabilities. Colloquial and fragmented expressions increase the difficulty of dialogue comprehension. The system needs strong contextual understanding to accurately grasp user intent across multiple turns. Furthermore, attachments pose additional challenges for data preprocessing and information extraction. Prompt design must account for these characteristics, guiding the model to perform accurate knowledge retrieval and response generation within the specialized domain, preventing hallucinations or misleading information.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`maxContext`	`6` turns	WeChat Work group conversations are typically short. 6 turns cover most contexts, avoiding information redundancy.
`Chunk size` (Segment Length)	`300 characters` (characters)	Balances the completeness of specialized terms with segment processing efficiency, preventing information loss from long texts.
`Recall count` (Recall Count)	`8 entries` (items)	Increases the scope of relevant knowledge snippet recall, improving hit rates for complex questions.
`Similarity threshold` (Similarity Threshold)	`0.75`	Ensures recalled knowledge snippets are highly relevant to the query, filtering out noise.
`Rerank result count` (Reranked Return Count)	`3 entries` (items)	Focuses on the most relevant knowledge snippets, reducing model processing burden and improving response quality.
`temperature`	`0.5`	Ensures response accuracy and consistency, reducing the risk of generating irrelevant content.

Three Common Mistakes

Dialogue window displays "Knowledge base retrieval failed" or "Could not retrieve relevant information": This occurs because specialized terms or entities in the dataset were not correctly identified and indexed, leading to retrieval mismatches.
After a user uploads a file or text dataset, the knowledge base fails to update or appears empty: This is due to PARSE_FILE_TIMEOUT_SECONDS being set too low, or the file encoding format utf-8 not being correctly recognized, causing file parsing to time out or fail.
In multi-turn conversations, the system "forgets" drug batch numbers or experimental parameters mentioned earlier by the user: This happens when maxContext is set too low, or the prompt does not emphasize maintaining context for key entities.

How to Confirm Correct Configuration

Simulate multiple in-group conversations containing biomedical specialized terms and multi-turn follow-up questions. Observe if the system consistently understands context and provides relevant responses.
Upload typical documents (e.g., drug instructions, experimental reports). Use keywords and semantic queries to check if the knowledge base accurately recalls corresponding passages. Verify the reasonableness of the Similarity threshold (Similarity Threshold).
Conduct small-scale grayscale testing in actual group chats. Collect user feedback and adjust Recall count (Recall Count) and Rerank result count (Reranked Return Count) based on feedback. Observe response quality and user satisfaction.

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.