Build Retrieval-Augmented Generation systems that combine large language models with external knowledge bases for accurate, up-to-date responses.
Retrieval-Augmented Generation (RAG) systems enhance LLMs by providing them with relevant external information during generation. This approach improves accuracy and enables models to access current information.
Understanding RAG architecture and components
Learning vector databases and embedding techniques
Implementing document chunking and indexing strategies
Building retrieval and ranking systems
Optimizing RAG pipeline performance
January 16, 2024
ai engineer
data engineer
backend developer
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