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Business Potential: 4 Key Applications of LLM (Large Language Models) 🚀

27 November, 2023


What Are Large Language Models and How Are They Trained?

LLMs are complex math functions that turn what you say into a meaningful response. To teach them, researchers first collect lots of example conversations. These include common phrases, questions, and in-context sentences along with the best answers.


The examples are then prepared so computers can read them. This means changing text into numeric codes, as LLMs only understand numbers. Words like "to", "of", and "and" are removed to simplify things and make it faster.


Next, the right model is chosen based on the examples and the goal. Training works by feeding an example and the correct answer into the model. It makes guesses using math rules. At first, most will be wrong so it learns from the right answers. An algorithm is used to adjust the model's rules to reduce mistakes. This repeats until the model can accurately match inputs to outputs.


Once trained, the model can be used to predict by applying new examples to its rules. While taking time and being complex, the process gives LLMs superpowers. It allows them to comprehend and respond to human language in a way not possible before - quickly, clearly and helpfully. The examples teach them, the math enables understanding, and the training refines this into an effective assistant.


1. Chatbots for Customer Support 🤖


Conversational AI helps companies deliver highly customized service. Chatbots get to know each person so they can respond based on individual wants and needs. This technology provides a far better experience than traditional options. Wait and handle times are drastically reduced as chatbots can assist huge numbers of people at once. Customers spend less time repeating themselves too, with conversations wrapping up quickly in far fewer steps.


Most importantly, satisfaction soars. Customers feel truly understood and cared for when chatbots offer personalized guidance. Meanwhile, businesses gain the flexibility to easily expand support as demand increases.


In the end, conversational AI ensures every person is treated as a unique individual. Companies can focus on each customer's specific situation, building stronger relationships through personalized and efficient assistance delivered at massive scale.


Companies are leveraging LLM-powered chatbots to provide high-quality, round-the-clock customer support, efficiently gather customer data, and reduce payroll costs associated with human representatives. Virtual assistants based on LLM can handle complex queries, offer personalized recommendations, thereby enhancing user experience.


For instance, Essent, an energy company, attributes 59% of all customer queries to its chatbot. Energy companies, including Essent, face high demand for customer service requests every day. For over a century, Essent relied on phone support as customers' sole option. However, growing competition in the Netherlands and surging requests meant this approach risked falling behind.


Essent recognized the need for bold change. In addition to boosting efficiency and lowering expenses, leadership saw chatbots as a way to revolutionize service. Customers could get quick, customized help through digital conversations instead of lengthy calls. By embracing this new technology, Essent empowered people with streamlined assistance their way. At the same time, the company gained the agility needed to satisfy rapidly increasing demand. No longer dependent on expensive phone systems, Essent transformed operations to remain ahead of shifts in customer expectations. It was a winning formula that proved adaptable companies thrive.


2. Sentiment Analysis and Trend Forecasting 📊


LLM enables businesses to analyze public sentiment, track brand perception in the market, and predict trends. This analysis aids enterprises in optimizing marketing strategies and building trust with customers. Platforms like Sprinklr offer services that utilize LLM for sentiment analysis and trend forecasting.


The social media management platform that utilizes natural language processing (NLP) and machine learning (ML) algorithms to analyze sentiment and trends on social media. NLP is used to extract insights from unstructured data such as text, while ML algorithms are used to analyze and predict trends. By combining these methods, Sprinklr provides businesses with an overall view of their brand's reputation and helps them make data-driven decisions.


3. Applications and Services with Personalized Recommendations 🛍️


E-commerce platforms and applications are integrating LLM into their processes to provide users with personalized recommendations. These models analyze user behavior and preferences to curate unique content, products, and services. For instance, Instacart, leading food delivery service developed "Ask Instacart" - a groundbreaking AI tool helping customers shop smarter.


By tapping into OpenAI's language prowess and Instacart's massive catalog data, it invites people to simply ask questions instead of searching. Whether seeking recipe ideas, product details, or dietary advice, Ask Instacart efficiently tackles the daily dilemma of "What's for dinner?". Integrated into the app's search bar, it intuitively organizes recommendations while sharing preparation tips, attributes, and considerations.


Rolling out now across the US, Ask Instacart saves time during grocery planning. It inspires new routines through personalized suggestions spanning over 80,000 store items. Customers receive tailored guidance that streamlines decisions throughout their shopping experience.


By leveraging conversational AI, Instacart revolutionized how people get grocery information. Ask Instacart empowers customers as informed, inspired decision-makers - reimagining assistance as a familiar yet powerful daily partner.


4. Document Assistance 📄


AI services like Google's Duet AI and Microsoft's Copilot are proficient in working with textual documents, spreadsheets, presentations, and emails. Businesses can use them to create quarterly presentations, organize email inboxes, or visualize textual data through charts.


In September of this year, Just AI introduced Jay CoPilot, which integrates LLM with image generation, synthesis, and speech recognition models in a user-friendly interface. This combination enables the service to prepare meeting minutes, transcribe and vocalize texts, create podcasts, generate analytical reports, and more.


Conclusion


As businesses increasingly adopt intelligent technologies, the impact of LLMs stands out: LLMs will transform how companies operate and grow. As these powerful models become more advanced, personalized and able to integrate diverse data types, they will continue streamlining processes and sparking new ideas across all industries.


Companies that adopt LLMs will have major advantages. They will strengthen customer relationships through personalized service, gain valuable insights to inform strategies, and scale up efficiently by automating routine tasks.


While challenges remain, conversational AI's impact will only increase. As issues around bias, transparency, and oversight are addressed, chatbots and other LLM applications will revolutionize how businesses serve customers and empower employees in the coming years.


For adaptable companies, LLMs offer huge potential. By leveraging the power of natural language, these models promise to reshape operations and pave the way towards a more connected, productive future.


In short, as the possibilities of LLMs continue unfolding, those who find ways to apply them will gain unmatched access to growth in the years ahead. The integration of these innovative technologies is set to transform business as we know it.

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