All about RAG Systems, LLMs, and Multimodal Models
The AI Landscape with Jerry Liu, CEO and co-founder of LlamaIndex
Good morning fellow AI enthusiast! This week's iteration focuses on a new episode of the What's AI podcast with Jerry Liu, CEO and co-founder of LlamaIndex!
Much of our discussion is devoted to the advantages of Retrieval Augmented Generation (RAG). Jerry explains why RAG is gaining popularity in the enterprise sector due to its simplicity, efficiency, and practicality. He also explains the differences between RAG and fine-tuning models, providing valuable insights on when each method is preferable, detailing the unique benefits of RAG in reducing costs and setup time, and minimizing hallucinations often seen in AI models.
We also tackle the technical side of things, discussing the importance of chunking strategies and data quality in RAG systems. Jerry's expertise shines through as he discusses embedding models, fine-tuning for specific domains, and optimizing retrieval processes. This part of our conversation is especially valuable for those interested in implementing or enhancing RAG systems in their own projects.
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Louis-François Bouchard
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