AI, Large Language Model

What Are LLM Apps?

Most people, by now, are familiar with LLMs (Large Language Models) like ChatGPT, Gemini or Claude. Many have also made use of an entirely new category of Applications called LLM Apps which make extensive use of LLMs to deliver new capabilities. 

A common criticism of many consumer LLM Apps is that they are simply wrappers around ChatGPT that OpenAI themselves will eventually implement. When dealing with simple text inputs or small volumes of images and PDFs, this criticism is entirely valid.

But as anyone who works in an enterprise setting knows, the business world is a lot messier and more complex than that. 

This is why the real value in LLMs comes when they are part of a broader platform that provides functionality to deal with the difficult specifics of each organization. This includes compliance, audit, reporting, data, systems, processes, workflows, channels, teams and people.

In this blogpost we’ll dive into LLM Apps, define them at a high-level and then dig into the practical details of what they are and how they can benefit your business.

What are LLMs?

A Large Language Model (LLM) is an advanced artificial intelligence system designed to understand, generate, and manipulate human language at a high level of complexity. Trained on extensive datasets comprising text from various sources such as the internet, books, and articles, LLMs utilize deep learning techniques, to process and produce human-like text. These models, such as OpenAI’s GPT-4, excel in tasks like natural language understanding, text generation, translation, and summarization. 

What Are LLM Apps?

 LLMs can be leveraged by businesses to automate customer support, generate content, perform market analysis, and enhance decision-making processes through their ability to interpret and generate contextually relevant information.LLM Apps are software applications that leverage Large Language Models (LLMs) to perform advanced language-related tasks. These apps utilize the capabilities of LLMs to understand, generate, and manipulate text, enabling a wide range of functionalities that enhance business operations. 

LLM Apps have three key strengths:

1. Understanding Queries: LLM Apps can comprehend intricate questions, requests and inputs, even when they’re not clearly structured. Unlike traditional systems that rely on set rules, LLM Apps interpret the meaning and intent behind an input, making them much better at handling diverse and complex inquiries.
2. Crafting Responses: LLM Apps can pull information from various unstructured sources, like PDFs, images, and knowledge bases, to create accurate and detailed answers. They don’t just follow a script; they gather and synthesize information from multiple documents to provide the best possible response.
3. Generating Insights: LLM Apps enhance enterprises’ ability to derive data and business insights by automating text analysis, data extraction, and processing. Ultimately, LLM Apps support strategic decision-making, enabling data-driven decisions and optimized operations.

These three attributes alone have been responsible for the current revolution in application development. We can now build LLM Apps that leverage these advanced capabilities of language models to address specific business challenges across core categories.

Categories of LLM Apps

We break LLM Apps down into three main categories:

AI Assistant Apps

AI Assistants can engage with your customers via voice, chat, or email with higher levels of accuracy and personalization. These customer-facing LLM Apps engage using natural language but also automate workflows and tasks, relieving human agents to focus on more complex customer issues.

Example: A telecommunications company uses an AI Assistant to handle routine customer inquiries about billing, service outages, and plan upgrades. By automating these interactions, human agents are freed up to address more complex technical issues, enhancing overall customer satisfaction and reducing wait times.

AI Copilot Apps

Working alongside your employees and agents, AI Copilots are role-based LLM Apps that boost efficiency and effectiveness. For example, they can proactively prompt agents on quality or compliance issues, help employees navigate complex workflows, or identify missing payments or documentation.

Example: In a financial services firm, AI Copilots can provide real-time guidance to customer service representatives during interactions, ensuring all regulatory requirements are met.

AI Agent Apps

AI or digital agents can work autonomously on defined tasks and workflows. These LLM Apps are highly task-oriented, performing advanced automation at scale. For example, they can analyze vast datasets to identify risks, summarize lengthy reports, or process documents without human intervention.

Example: A healthcare provider deploys AI Agents to process patient records, extracting relevant information for billing and insurance purposes. These agents can also generate summaries of patient visits, which helps doctors quickly review patient history during consultations.

Delivering Insights at Scale

One of the biggest surprises with LLMs in general is how an incredibly simple concept like “predict the next word” at scale was able to lead to revolutionary tools like ChatGPT.

In the same way with LLM Apps, a simple concept like “summarize this text” can lead to enterprise tools that have a material impact on your bottom line. For example, in the world of contact centers and customer support, the ability to summarize conversations instantly, no matter what the original source/channel, leads to never-before-seen improvements in agent efficiency.

The key term with so many LLM Apps is “scale”. Summarization at scale, call handling at scale, FAQs at scale, customer insights at scale, or recommended responses at scale. Each of these alone has a high impact. Together they can transform your business.

Example 1: E-commerce
An e-commerce company uses LLM Apps to manage customer inquiries about product details, order statuses, and returns. By integrating AI Assistants, the company has reduced response times and increased customer satisfaction, leading to higher conversion rates and repeat business.

Example 2: Legal Sector
In the legal sector, AI Copilots assist lawyers by summarizing case files, identifying relevant precedents, and even drafting legal documents. This reduces the time spent on routine tasks, allowing lawyers to focus on strategy and client interactions, ultimately improving the firm’s efficiency and service quality.

Example 3: Manufacturing
Manufacturing firms leverage AI Agents to monitor production lines, predict maintenance needs, and ensure quality control. By analyzing data from sensors and logs, these agents can preemptively address issues before they escalate, reducing downtime and maintaining high product quality.

Conclusion

LLM Apps represent a significant advancement in how businesses can leverage AI to improve operations, customer engagement, and decision-making processes. By understanding and implementing these powerful tools, companies can achieve unprecedented efficiency and effectiveness, driving growth and success in an increasingly competitive landscape. The future of business is intelligent, and LLM Apps are at the forefront of this transformation.