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Adel Zaalouk explains why Retrieval-Augmented Generation (RAG) remains essential in AI, highlighting its role in resolving limitations of Large Language Models (LLMs) such as the lack of access to up-to-date, verifiable knowledge. Key concepts such as parametric vs. non-parametric memory, the origins and components of RAG, and the impact of fine-tuning are discussed. Real-world use cases demonstrate RAG's effectiveness in customer support, legal research, and healthcare. The post also explores advanced RAG applications, like Agentic RAG, and outlines when to use standard RAG vs. Agentic RAG based on complexity and requirements.

RAG Reigns Supreme: Why Retrieval Still Rules!

Standalone LLMs Handicaps and RAG origins

Long-Context Models: A Powerful Tool, But Not a Replacement Retrieval/RAG

The Rise of Agentic RAG: Beyond Simple Retrieval