Understanding RAG vs Function Calling for LLMs

Large Language Models (LLMs) like OpenAI's ChatGPT and Google's Gemini have revolutionized productivity but face limitations, such as their inability to access real-time data or perform specific actions autonomously. To address these gaps, Retrieval-Augmented Generation (RAG) and Function Calling are two approaches that enhance LLMs' capabilities. RAG allows models to access external knowledge sources, thereby overcoming the limitation of fixed training data, while Function Calling enables models to execute predefined functions, bridging language understanding and operational execution. Implementing RAG involves creating a knowledge base and a retrieval system to fetch relevant information, whereas Function Calling involves defining function schemas and allowing the model to execute tasks through external systems. These approaches can be used individually or combined, depending on the task requirements, to customize LLMs effectively, making them more capable and practical for various applications.

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