Systematic program to prevent misleading recommendations
The following multidimensional protection measures are recommended for the health advisory characteristics of the RAG system:
- Data preprocessing: in
data_processing.pyThe medical evidence level filter is set in PubMed, and by default, only documents with clinical study level ≥2 in PubMed are adopted. - Two-wheel verification mechanism: in
app.pystart usingsafety_check=Trueparameter, the system automatically cross-validates the recommendations against evidence-based medical databases such as UpToDate - Interactive clarification: When a user question involves a complex combination of medications (e.g., "I'm taking warfarin, which vitamins should I take?") the system proactively asks for key parameters such as INR values.
- Risk labeling system: All high-risk recommendations involving prescription drugs, gene editing, etc. are automatically accompanied by an FDA warning label and a link to the reference.
- Local Cache Audit: Regular inspections
cache/directory, use theaudit.pyTools to Analyze Potential Bias Patterns
General users can verify the reliability of the advice with a simple "trustworthiness check passphrase": add the following before the question[v]Markers (e.g.[v]这个补剂建议是否有RCT研究支持？), the system returns the complete chain of evidence.
This answer comes from the articleRAG-based construction of a mini-assistant providing health advice (pilot project)The
May not be reproduced without permission:AI productivity tools " How to address potentially misleading advice from the LLM-RAG system in health counseling?
