AI and LLM: Unveiling the Profound Differences

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It wasn’t too long ago that artificial intelligence (AI) was the stuff of science fiction. In recent times, technology has developed rapidly and now looks poised to change the world as we know it.

Jargon and technical concepts can make AI and LLM appear to have a high barrier to entry. However, we will try to make things simple. In the guide below, we outline the differences between AI and LLM to give you a better understanding of this technology. Read on to learn more.

What is AI?

AI is an umbrella term used to describe a range of different advanced systems and technologies. Essentially, AI is designed to replicate human thinking and behaviors. The technology can be used for solving complex equations and mathematical problems, generating images or text, or simulating situations.

A key attribute of AI is its ability to learn. It collects and retains information as it is used, allowing it to improve its performance and generate more accurate, detailed responses in the future.

Despite being a relatively new technology, we’re seeing AI adopted by a range of different industries and sectors. The healthcare industry, for example, is reaping the benefits of AI. The tech means providers can deliver more personalized care programs for patients and optimize data handling processes.

What are LLMs?

As you read more about AI, you’re going to come across various terms and concepts. One term you’ll encounter regularly is LLM. These are Large Language Models, and they have generated a lot of interest in the past year as a result of high-profile systems achieving mainstream recognition.

LLMs are what are known as generative AI systems. This means they create content, in this case, text-based content, in response to user prompts. Developers train LLM models using huge volumes of data, giving models a foundation of knowledge that is used to generate responses.

What are LLMs Used For?

LLMs have a wide range of uses and they are being increasingly integrated by brands, businesses, and organizations all over the world. They can be used to create content such as blog posts, emails, and marketing copy and help businesses streamline administrative tasks.

In addition, LLMs are also used in programming. Developers use them to check lines of code or even write code from scratch. They are even used in the medical field, aiding with the development of medicines and vaccines through the analysis of protein and molecular structures.

ChatGPT has popularised LLMs and has propelled AI technology as a whole into the mainstream. Other powerful LLMs include BLOOM, Claude, Llama, and Orca.

AI and LLM: Learning and Adaptability:

  • AI: One of the key attributes of AI is its capacity to learn from data, improving performance over time. It adapts to new information, allowing for more accurate and detailed responses as it gains experience.
  • LLMs: Learning is a fundamental aspect of LLMs as well. They are trained on vast datasets to acquire a foundation of knowledge, enhancing their ability to generate contextually appropriate language responses.

AI and LLM: Industry Applications:

  • AI: Widely adopted across various industries, AI finds applications in healthcare, finance, manufacturing, and more. It contributes to personalized healthcare programs, data optimization processes, and automation of complex tasks.
  • LLMs: Primarily used for language-related applications, LLMs are integrated into content creation, programming assistance, and even in fields like medicine for analyzing molecular structures.


The terms AI and LLM are often used interchangeably. However, there are key differences between them that you should be aware of. AI is an umbrella term, while LLMs are a specific type of generative AI. The technology continues to develop and improve, so appreciating the differences between different models and systems will be key as we head towards an AI-powered future.