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Generative Search: Practical Advice for Retrieval Augmented Generation (RAG)
In this presentation, we will delve into the world of Retrieval Augmented Generation (RAG) and its significance for Large Language Models (LLMs) like OpenAI's GPT4. With the rapid evolution of data, LLMs face the challenge of staying up-to-date and contextually relevant. However, by harnessing the capabilities of vector embeddings and databases, LLMs can overcome these challenges and unlock their true potential.
Large Language Models, such as GPT4, are at the forefront of AI-driven advancements in natural language processing. To ensure their continued effectiveness, these models must adapt to ever-changing information. Vector embeddings, a powerful tool, are capable of capturing the essence of unstructured data. By combining these embeddings with sophisticated database search algorithms, LLMs gain access to a wealth of contextually relevant knowledge.
Principal Engineer @Redis
Sam Partee is a principal engineer at Redis helping lead the development and awareness of Redis in machine learning systems. Sam has a background in high performance computing and he previously worked at Cray and HPE on projects like SmartSim, Chapel, and DeterminedAI. In his spare time, Sam enjoys contributing to open source projects, writing on his blog, and spending time with friends and family.
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