Operational Guidelines for Improving the Accuracy of Health Advice
To optimize the accuracy of LLM-RAG-Longevity-Coach's recommendations, the following specific measures can be taken:
- Refinement of input data: Ensure that basic information such as complete genetic data, laboratory results, etc. are available, and that the system builds a personalized analytical framework from this data
- Refinement of the problem description: Use a "three-tiered description" when asking questions - give details of symptoms, duration, and what has been tried to improve them (e.g., "I have the ApoE4 gene variant, and my LDL has been consistently higher than 130 mg/dL for the past 3 months. dL and have tried the Mediterranean diet with little success").
- Enabling Intermediate Step Authentication: View specific data sources and suggestion generation logic for system searches in the Streamlit interface, and manually exclude searches with low relevance.
- Creating feedback loops: Flag inaccurate suggestions using the "Suggestion Correction" function and the system will dynamically adjust the subsequent search strategy.
- Regular updating of the knowledge base: By
git pullObtaining the latest version of the project to ensure that the latest medical research findings data are used
Advanced users can modify theconfig.yamlin the similarity threshold parameter (suggested range of 0.75-0.85) to fine-tune retrieval accuracy, but requires clinical knowledge background support.
This answer comes from the articleRAG-based construction of a mini-assistant providing health advice (pilot project)The
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