Exploring Three Key Strategies for Building Efficient Retrieval Augmented Generation (RAG)

Retrieval Augmented Generation (RAG) is a technique that uses an AI chatbot with personal data. Three key strategies to optimize RAG include smart text chunking, iterating on different embedding models, and experimenting with various LLMs or generative models. Smart text chunking involves breaking down text into manageable pieces for efficient retrieval by the Vector Database. Different techniques for this process include recursive character text splitting, small-to-big text splitting, and semantic text splitting. Iterating on embedding models determines how data is represented as vectors, which are crucial in AI applications. Lastly, experimenting with different LLMs allows users to choose the most suitable one for their workload.

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