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Artificial intelligence (ai), machine learning (ml), large language models (llms), and generative ai are all related concepts in the field of computer science, but there are important distinctions between them This article explores the fundamentals, key distinctions, and best use cases for both technologies. They have significant differences in their functionality and applications.
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These models are built upon complex architectures and trained on extensive datasets to handle a variety of linguistic tasks. Understanding the differences between genai and llms is crucial for businesses, researchers, and developers looking to integrate ai into their workflows In the rapidly evolving field of artificial intelligence, two terms often make headlines
Generative ai (gen ai) and large language models (llms)
While closely related, they are not synonymous Understanding their differences, capabilities, and applications is crucial for developers, businesses, and ai enthusiasts. In short, genai and llms are different in terms of the content they generate, how they are trained, and what they can be used for They also come with their own risks and requirements.
In this blog, we will delve into the basics of genai vs llm, discussing what they are, how they function, when to utilize each, and the practical examples that distinguish them. To put it simply, genai is a broad concept, while llms are one of its specialized tools Let’s break this down further. The history of ai’s mastery over language is a relentless pursuit of understanding
