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Category: LLMs

  • Retrieval Augmented Generation (RAG) with LLMs

    Retrieval Augmented Generation (RAG) is a technique that enhances the capabilities of Large Language Models (LLMs) by enabling them to access and incorporate information from external sources during the response generation process. This approach addresses some of the inherent limitations of LLMs, such as their inability to access up-to-date information or domain-specific knowledge. How RAG… Read more

  • Using .h5 model directly for Retrieval-Augmented Generation

    Using a .h5 model directly for Retrieval-Augmented Generation (RAG) is not the typical or most efficient approach. Here’s why and how you would generally integrate a .h5 model into a RAG pipeline: Why Direct Use is Uncommon: How a .h5 Model Fits into a RAG Pipeline (Indirectly): A .h5 model can play a role in… Read more