Data Characteristics for This Category
Dermatology product data primarily originates from pharmaceutical companies' official product inserts, clinical research reports, treatment guidelines published by industry associations, and medical journal literature. Data update frequency varies by source. Product inserts and guidelines typically undergo annual or quarterly revisions. Clinical reports are released continuously as research progresses.
In terms of document structure, product inserts usually include standard fields such as ingredients, indications, dosage and administration, contraindications, and adverse reactions. Clinical reports focus more on study design, subject characteristics, efficacy endpoints, and safety assessments.
Field specificities include: dosages often require unit conversion (e.g., mg/kg to g/m²); efficacy evaluations commonly involve specialized scoring scales (e.g., PASI, IGA); and strict requirements exist for allergen and active ingredient concentrations.
Constraints Imposed by These Characteristics on Multiturn Conversations and Prompts
The highly specialized nature and standardized document structure of dermatology product data require a multiturn conversation system to accurately identify medical terminology and product-specific jargon when understanding user intent.
Varying data source update frequencies mean the knowledge base needs regular maintenance and version control to ensure the timeliness and accuracy of consultation results. For example, when a user asks about contraindications for a specific product, the system must extract information from the latest product insert.
The unique units and formats for dosages and scoring scales require prompt design to guide the model in focusing on numerical context and unit matching, avoiding the generation of meaningless combinations. The emphasis on allergens and active ingredient concentrations in conversations constrains prompts to highlight this critical information to provide safe and effective advice.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 6000 characters | Ensures sufficient user query and model response history is retained in multiturn conversations, covering common product consultation scenarios. |
Chunk size (Segment Length) | 800 characters | Dermatology document paragraphs are relatively concise. This length helps maintain semantic integrity and prevents key information from being truncated. |
Recall count (Recall Count) | Top 5 | Given the precision requirements of dermatology product consultations, recalling the top few most relevant knowledge snippets is usually sufficient to support an answer. |
Similarity threshold (Similarity Threshold) | 0.78 | A higher threshold helps filter out irrelevant product or disease information, improving recall accuracy. |
Rerank result count (Reranked Return Count) | Top 3 | Further sorts the initially recalled results to highlight the products or solutions most aligned with user intent. |
TEMPERATURE | 0.3 | A lower temperature parameter encourages the model to generate more conservative, fact-based answers, aligning with the rigor required for medical consultations. |
Three Common Mistakes
- When querying specific product information, the model's response is vague or inaccurate. This usually occurs because the corresponding product insert in the knowledge base is outdated or lacks critical fields.
- When users ask for comparisons between different products, the system cannot provide effective advice. This often happens because the prompt fails to explicitly guide the model to integrate and compare information across multiple products.
- In multiturn conversations involving dosage or usage, the model generates values or units that do not match reality. This typically occurs because the prompt does not emphasize strict adherence to field units and numerical ranges.
How to Confirm Proper Configuration
- For core products, use different phrasings to conduct multiturn conversation tests. Check if the responses accurately cite the latest information from the product insert.
- Simulate users asking about the similarities and differences between various products. Observe if the model can reasonably compare based on knowledge base content and point out key distinctions.
- Input complex questions containing specialized medical terminology and dosage units. Verify that the numerical values and units in the model's responses are correct and conform to medical standards.
- Review conversation logs to confirm that the model consistently understands context in multiturn conversations and provides progressive responses based on previous dialogue content.
The values provided are common starting points. Measure them 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.