10 LLM Use Cases to Enhance Your Business

Large language models can recognize how sentences, characters, and words interact, enabling them to perform tasks such as translating text, performing sentiment analysis, and generating responses. These capabilities can be applied across various industries, roles, and tasks.

Explore the ways LLMs impact business productivity in nearly every industry. Ready to build your LLM skills? Consider enrolling in the IBM Generative AI Engineering Professional Certificate, where you'll learn key gen AI architectures and NLP models, and how to apply techniques like prompt engineering, model training, and fine-tuning.

Where can you apply large language models?

Large language models (LLMs) can be found throughout many industries. One of the first industries to use LLMs was health care, where they benefit patient outcomes through medical image analysis and predictive analytics. LLMs are also used in education to help improve student learning outcomes.

LLMs allow students to have a more personalized learning experience based on their 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 [1].

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.

Who uses large language models?

LLMs have a range of use cases, from generating content to creating the foundations for AI chatbots. They have the power to perform any number of tasks related to language use and can even automate everyday language tasks. 

LLMs are used by professionals across industries and in many roles. From creating unique customer experiences with chatbots to supporting scientific research in classification and creating meeting transcripts, LLM use cases are growing every day. However, some roles focus specifically on AI and large language models. Some specific jobs in data science train, develop, and use LLMs. Let’s look at a few LLM-focused jobs, their salaries, and how they interact with LLMs. 

1. Reinforcement learning researcher

Median total pay: $169,000 [1] 

Reinforcement learning (RL), sometimes called reinforcement learning from human preferences (RLHF), is a machine learning feedback mechanism involving human input in algorithm training. This can improve the language abilities of LLMs with complex human emotions or associations with language. 

2. Natural language processing engineer

Median total pay: $114,000 [2]

Natural language processing (NLP) is the basis for using LLMs. Training LLMs produces NLP tasks like translation, chatbots, and human language production. An NLP or machine learning engineer must understand the linguistic properties of human language and how to create machine-learning algorithms to replicate them. 

3. Deep learning scientists

Median total pay: $199,000 [3]

Deep learning allows for more complex uses for natural language processing, creating LLMs that replicate human speech in uses like chatbots. Deep learning algorithms allow for the recognition of text meaning and have the ability to replicate it, similarly to human language. 

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