Multi-Turn Conversations and Prompts for Mental Health

Data Characteristics for This Category

Mental health product data comes from diverse sources. These include clinical trial reports, drug inserts, patient education materials, professional treatment guidelines, and academic research papers. Data updates frequently, especially clinical trial progress and drug indication changes. Document structures vary. They range from structured drug inserts (containing fixed fields like indications, dosage, adverse reactions) to semi-structured clinical research reports (with sections like background, methods, results, discussion) and unstructured patient consultation records. The data often contains specialized medical terminology, generic and brand drug names, dosage units (e.g., mg, ml), treatment durations (e.g., weeks, months), and mental health scale scores (e.g., HAM-D, CGI-S).

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

The specialized nature and update frequency of mental health data require multi-turn dialogue systems to accurately understand medical terminology and maintain knowledge currency during conversations. The coexistence of generic and brand drug names, along with the precision required for dosage units and treatment durations, demands high accuracy in entity recognition and unit conversion. Semi-structured and unstructured documents necessitate more refined text segmentation strategies for knowledge base construction to ensure retrieval relevance. The vague and subjective nature of patient symptom descriptions requires prompt design to guide users to provide more specific information, while also identifying potential sensitive or urgent situations. In multi-turn conversations, the system must accurately track patient medication history and treatment progress. It must also understand complex medication instructions or adverse reaction inquiries based on context, avoiding misinformation.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Segment Length)	`500–800 characters`	Accommodates complex medical concepts and related information in mental health documents, ensuring complete context within a single segment.
`Overlap Length`	`50 characters`	Ensures contextual continuity at segment boundaries, improving accuracy for cross-paragraph information retrieval.
`Recall count` (Recall Count)	`8–12 items`	Balances retrieval efficiency and coverage, especially in multi-turn conversations where multiple aspects of patient inquiries need to be covered.
`Similarity threshold` (Similarity Threshold)	`0.75–0.85`	Ensures the professionalism and relevance of recalled content, avoiding the introduction of medically inaccurate information.
`maxContext`	`4096 tokens`	Accommodates the complexity of mental health dialogues, retaining sufficient conversation history to maintain contextual coherence.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Addresses the parsing requirements for large clinical reports or drug inserts, preventing timeout failures due to excessively large files.

Three Common Mistakes

Vague answers regarding drug dosages or treatment plans in conversations occur when knowledge base segments are too short or prompts fail to effectively guide the model to make comprehensive judgments based on context.
When users ask about the meaning of specific mental health scale scores, the system cannot provide accurate explanations. This happens when descriptions of scales in the knowledge base are not segmented independently or lack sufficient background information.
Processing large clinical trial documents results in excessively long parsing times or failures. This occurs when PARSE_FILE_TIMEOUT_SECONDS is set too low or the file processing pipeline lacks asynchronous optimization.

How to Verify the Configuration

Design test cases for typical mental health drug consultation scenarios. Include elements like generic and brand names, dosages, and adverse reactions. Observe the accuracy and completeness of dialogue responses.
Simulate patient descriptions of vague symptoms. Check if the system can guide users to provide key information through multi-turn questioning and ultimately offer relevant product suggestions or risk warnings.
Upload multiple large clinical trial reports and treatment guideline documents. Monitor parsing times under the PARSE_FILE_TIMEOUT_SECONDS parameter. Ensure all documents are processed successfully and are retrievable.
Randomly select professional medical terms from the knowledge base. Conduct retrieval tests using different phrasing. Evaluate the impact of Similarity threshold (Similarity Threshold) and Recall count (Recall Count) on the quality of retrieved results.

Note: The values provided are common starting points. Measure them against your own samples to determine the most suitable configuration.

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.