Balancing Economy and Accuracy in Technical Architecture
Compared to directly using a large language model to process health advice, the RAG architecture of LLM-RAG-Longevity-Coach achieves a double optimization through accurate data retrieval: it reduces the cost of API calls by 601 TP3T while increasing the accuracy of advice by 401 TP3T.The system builds a database of professional health knowledge vectors locally by retrieving only data snippets that are truly relevant to the users' questions The system builds a local database of specialized health knowledge vectors and retrieves only the data fragments that are truly relevant to the user's problem as the context of the LLM.
- Avoid transferring full database to LLM to save token consumption
- Retrieval filtering mechanism to exclude irrelevant information from interfering
- Dynamic context window optimization for best price/performance
Actual operation data shows that when dealing with typical genetic counseling problems, the traditional LLM solution requires 8,000+ token contexts, while the RAG solution requires only 1,200 tokens on average, dramatically reducing operation costs while maintaining the same level of professionalism.
This answer comes from the articleRAG-based construction of a mini-assistant providing health advice (pilot project)The
