10 LLM Use Cases to Enhance Your Business

Machine learning is impacting various industries, revolutionizing how people can use computers, enabling them to process massive amounts of data to learn to make decisions, and enhancing different types of applications. One technology making the most of deep learning, a specific type of machine learning, is large language models (LLMs). Large language models are capable of understanding how sentences, characters, and words work together to do things such as translate text, perform sentiment analysis, or generate responses.

What is an LLM?

A large language model is a type of deep learning model trained on massive data sets to help computers imitate human language. This leads to text generation, recognition, prediction, translation, and summarization capabilities. Deep learning models operate similarly to the human brain, processing information to become better at recognizing patterns and making predictions so that they can learn and think the same way we do. 

What differentiates deep learning models from standard machine learning models is that deep learning uses far more data points, relies on less human intervention to learn, and has a more complex infrastructure that requires greater computational power. Because of this, large language models are costly and aren’t as widespread as other machine learning models.

Where can you apply large language models?

Large language models can be found throughout many industries. One of the first industries to use LLMs is health care, where large language models benefit patient outcomes through medical image analysis and predictive analytics. Large language models are also used in education to help improve student learning outcomes. LLMs make it possible to provide students with a more personalized learning experience based on individual learning styles and preferences. Beyond health care and education, large language models are now notable in several areas, including the financial, entertainment, government, legal, and automotive industries.

10 LLM use cases for businesses

With the power of artificial intelligence and deep learning, large language models can perform a wide range of tasks and support different types of applications, whether for internal use or to improve customer experiences. Take a look at ten ways businesses can utilize large language models now.

1. Chatbots and virtual assistants

Chatbots and virtual assistants use large language models to provide quality service to customers. LLM chatbots are capable of providing assistance with troubleshooting and answering common questions. These chatbots can even analyze sentiment within the text to respond more effectively to customers and use predictive analytics to identify potential issues that the customer may be experiencing quickly.

2. Content writing

A notable feature of large language models is their text-generation capabilities. After going through massive amounts of training data, LLMs can understand languages and context around the words, making developing written material possible and comparable to text written by humans. 

3. Talent acquisition and recruiting

Businesses can use large language models to sift through job applicant information and identify the candidates best suited for the job. Not only does this help with identifying quality candidates, but it also makes the entire process far more efficient. Using LLMs in the hiring process can also improve workplace diversity as it eliminates unconscious bias.

4. Targeted advertising

Along with the content creation capabilities of large language models, a specific circumstance where this is beneficial is for developing targeted marketing campaigns. LLMs make it possible for you to identify trends and better understand your target audience, leading to opportunities to develop more personalized advertisements and product recommendations.

5. Social media

You can use large language models to develop social media posts and come up with unique captions to go along with posts that include visual content. Large language models can analyze social media content to understand how to create material that people are more likely to engage with. 

6. Classifying text

Large language models can understand the relationships between words in order to classify text that shares the same sentiment or meaning. By taking text and sorting it into predetermined categories, it's possible for you to organize information from different types of documents and more effectively utilize unstructured data.

7. Translation

Large language model translation capabilities help businesses expand their reach globally to new markets where potential customers speak another language. You can use LLMs to translate various materials, such as website content, marketing materials, product information, social media content, customer service resources, and even legal agreements. 

8. Fraud detection

Large language models are revolutionizing fraud detection, improving the efficiency of determining whether or not a transaction is potentially fraudulent, predicting customer transactions to block transactions deemed fraudulent, and assessing the risk level present. LLMs can quickly spot suspicious patterns and protect your business by analyzing huge amounts of data.

9. Supply chain management

Large language models help contribute to supply chain management practices thanks to their analytics and predictive capabilities. With LLMs, you can gather insight to manage inventory, find vendors, and analyze the market to understand demand levels better.

10. Product development

During product development, large language models support several stages, beginning with the ideation phase and throughout the production process, identifying opportunities for automation and even contributing to decisions such as what production materials you should use. LLMs are also useful for performing testing and exploratory data analysis during the research stage of product development.

Getting started with Coursera

Discover learning opportunities on Coursera to explore different areas of artificial intelligence, including large language models and deep learning. Generative AI with Large Language Models from DeepLearning.AI is designed to help you learn fundamental generative AI and LLM concepts, including deployment methods, model selection, and training techniques.

Introduction to Deep Learning and Neural Networks with Keras from IBM will introduce you to deep learning models and how they compare to neural networks while also providing an opportunity to practice developing deep learning models.