Exploring Diverse LLM Use Cases: Industry Specific Applications of LLMs

By Snigdha | August 24, 2023 6:47 am

According to Deloitte (2022), 94% of business leaders believe that AI will be important for a business to be successful (Source). NLP or Natural Language Processing is a branch of this esteemed field with a global revenue expected to cross $43 billion by the year 2025 (Source). With great leaps of advancement in the field of NLP, large language models or LLMs have emerged to great significance. LLMs have demonstrated remarkable capabilities in understanding and generating human-like text, revolutionizing various industries. In fact, by fine-tuning LLMs, it is possible to cater to any industry with tailor-made solutions. AI-driven no-code platforms further expand the potential of businesses across industries, helping them reach targets, otherwise impossible to even think about. Irrespective of the industry, from healthcare to finance, legal to entertainment, or more, it is possible to customize LLMs to offer the best-suited solutions in record time. Due to their inherent flexibility, large language models have found use in diverse industries and that is what we are going to talk about - LLM use cases across industries.

  1. Customer Experience And Support

  2. Large language models have transformed the landscape of customer experience and support. The integration of LLM-powered chatbots and virtual assistants has brought about a new era of efficient and responsive customer interactions. These AI-driven assistants are available around the clock, offering immediate responses to customer inquiries and concerns. For instance, a telecommunications company can leverage LLMs to deploy a chatbot on its website and mobile app. Customers seeking assistance with network issues, plan upgrades, or billing inquiries could interact with the chatbot in real time. The LLM-driven chatbot analyzes the user's queries and provides accurate and relevant responses instantly. This saves customers time and reduces the burden on human support agents, letting them focus on more pressing matters.
  3. Social Media

  4. LLMs have brought about a significant transformation in the way social media platforms operate. By implementing LLM-driven content moderation systems, platforms can automatically identify and filter out any inappropriate content, including hate speech, offensive comments, etc. This ensures a safer and more welcoming environment for users to engage and interact. Consider the example of Instagram when it comes to content moderation. Its LLMs analyze comments and captions in real-time, and flag those that violate community guidelines. Doing this prevents any harmful content from reaching a wider audience and maintains the platform's integrity. Additionally, LLMs work towards improving user engagement by suggesting personalized content based on a user's browsing history, behavior, interactions, and preferences.
  5. E-commerce And Retail

  6. E-commerce and retail industries have embraced LLMs to transform the way consumers interact with online shopping websites and mobile apps. LLMs facilitate several key functions, such as generating product descriptions, reviews, and personalized product recommendations. For example, an e-commerce platform like Amazon uses LLMs to automatically generate detailed and enticing product descriptions. LLMs can analyze product specifications, user reviews, and other relevant data, to create comprehensive and engaging descriptions to help customers make informed purchasing decisions. Moreover, LLMs improve the shopping experience through virtual shopping assistants. These AI-driven assistants guide users through their shopping journey, offering product recommendations, answering queries, and providing a personalized touch. This level of personalized assistance improves customer satisfaction and increases the likelihood of conversions.
  7. Finance

  8. With LLMs making their way into the finance industry, there has been a literal revolution in the way things operate in this industry. Different LLM models can be customized for critical activities like fraud detection, risk assessment, and investment analysis. LLMs have the ability to process vast amounts of financial data and deliver valuable insights that help with critical decision-making processes. For example a financial institution can employ LLMs to detect fraudulent activities by analyzing historical transaction data and identifying unusual patterns. These activities can then be flagged for later scrutiny. This approach minimizes financial losses and safeguards customer accounts. Additionally, LLMs can process news articles, market reports, and economic indicators before conducting an investment analysis. For example hedge funds can use LLMs to quickly analyze a wide range of data sources and generate insights that guide their trading strategies saving time and improving the accuracy of data-driven financial decisions. In a recent Forbes survey, answering financial questions was cited as the top use of AI with 43% respondents voting for it (Source).
  9. Marketing and Advertising

  10. LLM integration has had a massive impact on the marketing and advertising landscape. These models are harnessed to streamline content creation, optimize ad copywriting, and enhance customer targeting. When it comes to content creation, LLMs are adept at generating compelling and engaging content in any format. For example, ad agencies can input key information about their products or services, and the LLMs will produce persuasive ad content tailored to the target audience. This accelerates the content creation while ensuring that the messaging resonates with potential customers. LLMs can also contribute to create personalized marketing campaigns by observing and analyzing user behavior and preferences. Advertisers can leverage this information to optimize ad placements, ensuring that their campaigns reach the right audience at the right time. This accuracy and efficiency increases the effectiveness of marketing efforts and the return on investment.
  11. Cyber Law

  12. Upon LLM integration into Cyber law, it has seen significant advancements and has emerged as one of the most massively popular LLM use cases. LLM models play a central role in legal research, contract analysis, and compliance assessment, improving the efficiency and accuracy of legal processes. For example, the case of a law firm dealing with contract review. LLMs can quickly analyze and extract key information from contracts, identifying potential risks and legal issues. This expedited process saves time and ensures that leveryone involved can focus their expertise on addressing complex legal matters instead of the more mundane activities. Additionally, LLMs help people stay up-to-date with changing regulations and legal developments. By processing and summarizing legal documents, LLMs provide legal professionals with concise and relevant insights.
  13. Healthcare

  14. LLM integration in the healthcare sector has been profoundly impactful, with its applications ranging from medical research to patient communication and diagnosis assistance. Large language models can help process and analyze vast volumes of medical literature and research papers. This speeds up the process of discovery of relevant studies and findings, helping researchers stay informed about the latest advancements in their field. These models even contribute to patient communication by offering easily understandable explanations of medical conditions and treatment options. Patients can enter their symptoms and questions, and the LLM-driven systems can offer detailed and jargon-free responses, empowering patients to make informed decisions about their healthcare.


Though the LLM use cases listed above give you a good idea about the potential of applications of LLM across industries, there is a lot more that can be achieved by integrating LLM in a business of any scale or size. Like any other technology there are a few challenges in fine tuning LLM. To overcome these challenges make sure that the best practices in fine tuning LLM are followed at all times before deploying the integration.

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