AI Explained: What Is a Large Language Model and Why Should Businesses Care?

Artificial intelligence (AI) has revolutionized how businesses operate, making tasks more efficient, processes smarter, and interactions more seamless. At the heart of many of these advancements is a type of AI called a large language model (LLM). If you’ve used AI tools like OpenAI’s ChatGPT or Anthropic’s Claude, you’ve already encountered an LLM.

But what exactly is an LLM, and why does it matter for your business?

To understand LLMs, it’s first crucial to know what an AI model is and how it’s different from traditional software programs such as Microsoft Word. A software program contains instructions — code — for the machine to tell it how to execute tasks, such as performing math calculations. However, it can only do what it is explicitly programmed to do.

An AI model is different. Most, but not all, find patterns in the data you give it — a process called machine learning. In an AI model’s training phase, its architecture provides a framework for learning, ingests the data you give it and finds patterns. Then, it develops its own internal representation of these patterns. With these internal representations, it can look at new data and analyze it — a process called inference.

Think of an AI model as a student and his textbooks as the data. The student learns from his textbooks and internalizes this knowledge. The student gets hired in a new job and applies this knowledge (for example, accounting) to analyze or handle new data — such as quarterly sales or other business information the company provides.

What Exactly Is an LLM?

A large language model is an AI model trained on vast amounts of text, such as the entire internet. It’s as if someone has read millions of books, articles, blogs and messages in a dataset. The AI model learns to find statistical relationships between words and phrases through this training.

The model’s knowledge is encoded in its parameters, or mathematical values learned during training. The more parameters an AI model has, the larger and potentially more sophisticated it is. The LLM determines how words fit together, how ideas are expressed and how to respond to questions or prompts.

For example, when someone starts writing “how are,” the model predicts “you?” as one of the most likely next words based on common language patterns. Since it generates “new” content, an LLM is classified as generative AI.

Initially, LLMs were mainly text-based models that could not understand images, audio or video. However, they can become multimodal LLMs that process text and images as inputs and outputs. The later series of OpenAI’s GPT LLMs, like GPT-4, is a multimodal model. Text-only LLMs become multimodal when their architecture is modified, and they are fine-tuned to integrate different modes of content (audio, video, images).

Old AI vs New AI

Generative AI is a fairly recent development in artificial intelligence, whose creation harkens back to the 1950s or earlier . Companies have been using AI for decades; these older AI systems are rule-based, expert systems that have predefined rules and logic to make decisions and solve problems.

They also include early natural language processing and classical robotics and vision systems. Organizations still use older AI today. A general way to differentiate old and new AI is this: Old AI uses predefined rules, while new AI is not explicitly programmed. Rather, new AI learns from the data (via machine learning, neural networks and pattern recognition) and it is more dynamic, flexible and adaptive than old AI.

But with its probabilistic nature, new AI brings new challenges. It can hallucinate, or make things up. It can introduce bias in its responses, since it may inherit them from real-world data. They can breach privacy, mimic copyrighted works, and be misused in cyberattacks or misinformation. Another issue is that AI models consume high energy, raising environmental issues. There are also concerns that new AI will lead to job losses.

Another thing to know: In new AI, foundation models are large models trained (or pre-trained in industry parlance, since it is the starting point) on huge datasets for a broad range of tasks for a variety of applications. Examples of foundation models are OpenAI’s GPT series, Meta’s open-source Llama family and Google’s Gemini family of models.

These foundation models are usually trained again for specific purposes, like analyzing medical X-rays. This further stage of training is called fine-tuning. Fine-tuning ushers in AI models that are useful to different industries, be it healthcare, finance, retail and others.

LLMs in Businesses

With their ability to learn from vast amounts of data at a scale no human can replicate, LLMs are transforming the way businesses operate. LLMs enable organizations to become more efficient, cut costs, and enhance innovation, whether it’s automating routine tasks, enhancing customer engagement, or unlocking insights from data. However, businesses must use LLMs strategically by choosing the right tools, training employees to use them effectively, and staying vigilant about ethical pitfalls.

Use cases for LLMs include:

Customer service: LLM-powered chatbots can answer questions in natural language even if they weren’t preprogrammed on the answers. It is a stark departure from old chatbots that only provide canned answers and, if the question is outside its programming, fail to provide a satisfactory answer.

Content creation: LLMs can be used throughout the organization to write blogs, social media posts, product descriptions, meeting summaries, among others.

Smarter business processes: It can transform how departments work, such as HR, sales, finance, IT, legal, among others.

Smarter data analysis: LLMs can analyze and summarize long reports and extract key insights to aid business leaders in decision-making.

Marketing personalization: LLMs can help craft tailored messages to improve engagement and conversion rates by analyzing customer data.

Training and development: Companies can use LLMs to simulate real-world scenarios for employee training.

Research and development: LLMs can be used for data analysis, running simulations, and generating hypotheses. It can be used to create new products.

Cybersecurity: LLMs can be used to analyze network traffic, email patterns and user behavior to detect anomalies and flag hacking or phishing attempts.