How to Enhance the Accuracy of the Llama Q&A System Using RAG?(LLaMA 3 Practical 12)
Learn how to optimize the RAG system's performance, ensuring accurate, timely content generation with real-time data, user feedback, and source transparency.
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Welcome to the "LLaMA 3 Practical" Series
In the previous lecture, we learned how to efficiently build indexes through LLaMa 3 to enhance the input quality of the RAG system.
In this lecture, we will explore how to optimize the online retrieval performance of the RAG system to ensure the accuracy and timeliness of the generated content.
Default Behavior when Knowledge Conflicts
During the pre-training process, LLaMA 3 accumulates a large amount of knowledge, which is mainly divided into two categories.
The first category comes from the historical real-time information in the raw corpus, reflecting facts, events, or viewpoints at specific points in time. However, this information may lose its timeliness as time progresses.
The second category is knowledge that the model derives from learning the patterns in the raw data. This knowledge is usually not direct facts, but statistical associations and inferences about language or concepts.