What You Need to Know About Large Language Models

Large language models (LLMs), are reshaping the way businesses interact with data, automate processes, and communicate. 

Initially, AI in business was synonymous with basic automation and structured data processing, but LLMs have taken these capabilities to a whole new level. From drafting personalized content to powering conversational agents, these models are transforming industries by handling tasks once thought to be reserved strictly for humans.

In 2024, the adoption of LLMs skyrocketed, with  65% of companies reporting their use in at least one operational area, up from just 50% in 2023. Another recent study found that using LLMs reduces task completion time by 37% and can even improve output quality by up to 18%

However, like any groundbreaking tech, LLMs come with potential value and a steep learning curve. What exactly makes them different from previous AI technology? And how can businesses harness their power to innovate and stay ahead?

Understanding Large Language Learning Models

In short, LLMs are advanced artificial intelligence systems designed to process and generate human-like text by leveraging datasets and sophisticated neural network architectures.

How Do LLMs Work?

LLMs are primarily built upon the transformer architecture, introduced in the research paper, Attention is All You Need, in 2017. Unlike earlier recurrent neural networks and long short-term memory networks, transformers use self-attention mechanisms to process input data in parallel, which is much more efficient for capturing long-range dependencies in text. 

The transformer architecture of many LLMs comprises an encoder-decoder structure, where the encoder processes input sequences, and the decoder generates corresponding outputs. However, other LLMs, such as OpenAI's GPT series, employ a decoder-only Transformer model, focusing solely on generating text based on input prompts.

It’s All in the Training Data

The performance of LLMs heavily depends on the quality and diversity of their training data.

Models like GPT-3 were trained on hundreds of billions of tokens from sources including Common Crawl, WebText, books, and Wikipedia, enabling them to learn a wide array of language patterns and factual information. The training data results in the construction of parameters, or the learned values from the data that defines how the model functions.

LLMs use deep neural networks with billions of parameters. For instance, GPT-3 contains 175 billion parameters, with GPT-4 and Gemini 1.5 having trillions of parameters — the more parameters, the more complex the language patterns it can create. The magic happens because these neural networks are trained to predict the next word in a sequence, learning contextual relationships and linguistic structures based on their training data.

Why Business Leaders & IT Professionals Should Care

LLMs are reshaping business operations by enhancing innovation, decision-making, and efficiency. Their integration offers significant economic benefits and a competitive edge, but also introduces challenges related to data security and ethical considerations.

Having the ability to process and generate human-like text with such ease allows organizations to automate tasks, optimize workflows, and extract valuable insights from extensive datasets. Since LLMs can help automate routine tasks, they reduce manual effort and minimize errors. For fields like customer service, AI-driven chatbots can help human agents handle inquiries more efficiently, that it can serve as a competitive advantage.

Leveraging AI to automate repetitive and time-consuming tasks can also significantly reduce operational costs. For example, Klarna, a Swedish buy-now-pay-later company, reported an 11% decrease in sales and marketing expenses in the first quarter of 2024, attributing 37% of this reduction — approximately $10 million annually — to the use of AI. 

Applications of LLMs in Business

Many businesses have been quick to put AI to use to help out in day-to-day operations. Their ability to understand and generate human-like text enables companies to automate and optimize various functions, and save time wherever language or data is involved. 

Customer Support

LLM-powered chatbots provide round-the-clock assistance, handling a multitude of customer inquiries simultaneously. For instance, Bank of America utilizes its AI-driven virtual assistant, Erica, to offer instant information and facilitate transactions. So far, the company says that it has helped millions of users and significantly improved the customer experience and operational efficiency.

Companies implement AI chatbots to automate responses to frequently asked questions, reducing the workload on human agents and ensuring consistent information delivery. For example, Fandango employs chatbots to manage a high volume of customer inquiries efficiently, providing instant responses and improving overall service quality.

Content Creation

Marketing content is another area where LLMs can come in handy. AI-driven platforms help businesses in generating engaging marketing materials. Or for businesses that already have an existing library of content, LLMs can optimize it for better performance by analyzing SEO trends, keyword usage, and user engagement data. 

Massive name brands, such as The Washington Post and Bloomberg, have been using LLMs to generate content for marketing campaigns and stories for some time now. The results have shown a significant increase in output and, as a result, a corresponding increase in market share.

Since they can quickly synthesize complex information into clear and accessible formats, LLMs are perfect at creating other types of content. For areas outside of marketing, LLMs are reliable for producing developer documents: structured and coherent technical documentation, including user manuals, troubleshooting guides, and software instructions.

Data Analysis

LLMs’ capabilities in summarizing datasets mean they can be a great data analysis assistant. LLMs can process vast amounts of data, extracting key insights and trends. A good example of this in action is Irell & Manella, a law firm that has developed in-house AI tools to analyze extensive legal documents, improving efficiency and client service.

HR & Recruitment

Having an LLM assistant can also come in handy for the HR and recruitment department. LLMs can help analyze resumes and cover letters  more efficiently, identifying keywords and evaluating qualifications against job descriptions. 

On the candidates’ side, LLMs can also be a huge help. Writing resumes can take a long time, and writing cover letters for each job application can take even longer. Putting AI to work to handle this entire flow, generate unique cover letters for each job, and tailor resumes to be specific for each application, can save valuable time while on a job hunt.

Workflow Automation

Integration with business tools and custom scripting capabilities means that you can get an LLM to interact with all sorts of different platforms. Salesforce, Asana, and Slack — any platform can be integrated with an AI to create more efficient systems.

In CRM tools, LLMs can draft customer responses, analyze interactions, and generate actionable insights, ensuring that sales and support teams operate at peak performance. In project management platforms, they take on tasks like summarizing updates, assigning responsibilities, and tracking deadlines, helping teams maintain momentum without manual intervention.

Leading LLMs in the Market Today

OpenAI’s GPT Series

OpenAI's GPT series is among the most widely recognized large language models in the AI landscape. Since the release of GPT-2 in 2019, OpenAI has continually pushed the boundaries of natural language processing. GPT-4, the latest in the series, is capable of handling complex tasks such as coding, drafting long-form content, and even nuanced conversation with contextual depth.

Gemini

Google's Gemini represents a new generation of LLMs that combine conversational capabilities with advanced reasoning. Launched in December 2023, Gemini is part of Google Cloud's initiative to bring AI-powered solutions to enterprises.

It integrates deeply with Google's suite of services, such as Workspace, providing developers with tools to build customized AI-driven applications. With features like multimodal processing — the ability to handle text, images, and other data types — Gemini stands out as a versatile option for business use.

Anthropic’s Claude

Claude, the “ethical AI,” is Anthropic's answer to GPT and Gemini. It is designed with safety and ethical AI considerations at its core. Founded by former OpenAI researchers, Anthropic focuses on creating AI systems that are aligned with human values.

Claude is known for its transparent decision-making processes and advanced comprehension of complex instructions. Many claim it has a knack for coding, so it is frequently used in environments that take advantage of that, such as technical documentation and e-commerce chatbots.

Grok

Grok is a generative AI chatbot developed by xAI, a company founded by Elon Musk in 2023. Designed to provide real-time information with a humorous touch, Grok integrates directly with X, allowing it to access and process up-to-date data from the platform. The chatbot incorporates a witty and humorous tone, which sets it apart from other AI chatbots, but makes it a less ideal choice for business use. 

Meta’s LLaMA

Meta's LLaMA (Large Language Model Meta AI) is a family of foundational models designed to push the boundaries of open science in AI research. Unlike many proprietary models, LLaMA is open-source, allowing researchers to access its architecture and training methodologies for experimentation and academic advancement. 

LLaMA 2, the most recent version, offers improved scalability and performance, handling tasks such as text summarization, translation, and context-aware conversations with greater precision. Meta’s commitment to transparency and collaboration has made LLaMA a valuable resource for AI innovation.

Get Started With Large Language Models

LLMs have brought about a paradigm shift in how businesses approach communication, data analysis, and operational efficiency. For businesses willing to take the leap into implementing them in their business operations, they have the potential to boost productivity and cut costs across the board.

At Promevo, we understand that adopting cutting-edge AI like LLMs isn’t just about implementation — it’s about preparing your teams to thrive with these powerful tools. 

Ready to see how LLMs can drive innovation and give your business a competitive edge? Contact us today to discover how we can help you take full advantage of this new technology.