5 Real-World Use Cases for LLMs

Nowadays, every business needs efficient and intelligent solutions to automate routine processes, manage complex systems and improve decision-making. This is where Large Language Models (LLMs) step in. With LLMs, businesses can boost their operations by automating everything from technical queries to customer support, faster than ever before. In our latest article, we explore real-world use cases of LLMs where leading companies have adopted LLMs to improve their operations. 

Improving Business Operations

Ava, Instacart’s internal AI assistant is a prime example of how large language models (LLMs) can drive productivity in enterprise settings. It is a versatile tool for Instacart employees, enhancing efficiency across multiple departments, including Engineering, Operations, Recruiting and Marketing. Its web interface, similar to ChatGPT provides features such as automatic model upgrades, full-text conversation search and the Ava Prompt Exchange, where employees can share and reuse optimised prompts. Engineers at Instacart use Ava to generate and debug code, while other teams rely on it for summarising meetings, drafting communications and answering company-specific queries.

Just like Ava, LLMs offer broad applications in enterprises to improve efficiency and scalability across various functions. They can automate customer support, generate insightful reports and refine sales strategies with personalised communications. But that’s not all, LLMs accelerate research with real-time knowledge retrieval, so teams can access relevant data quickly. These capabilities improve productivity by reducing manual tasks, improving faster decision-making and team collaboration. With their ability to process and analyse vast amounts of information, LLMs help streamline workflows and support data-driven business growth.

Handling Technical Queries 

Mercado Libre is one of Latin America's largest e-commerce platforms which has successfully developed an internal tool powered by Large Language Models (LLMs) to boost developer productivity. This AI-driven tool efficiently answers technical questions related to the company's technology stack and automates documentation creation. By incorporating LLMs, Mercado Libre has changed how its developers interact with internal resources to streamline workflows and boost overall efficiency. The LLM itself was trained on an extensive collection of internal documents and codebases, which allowed it to provide highly relevant and accurate responses, with fine-tuning playing an imperative role in enhancing its effectiveness

LLMs have vast potential beyond internal tools. For example, in healthcare, LLMs can assist with medical research and patient record analysis and even support telemedicine by providing instant and relevant information. In finance, LLMs can help in fraud detection, risk assessment, and automating customer service. 

Product Attribute Extraction (PAE)

Managing product catalogues is a massive challenge for retailers and Walmart has tackled this issue head-on with LLMs. The company developed a Product Attribute Extraction (PAE) engine designed to onboard new products while extracting key attributes from existing product catalogues. By analysing text and images from PDF documents, Walmart’s AI-powered system identifies essential product attributes and categorises them accordingly. This helps retailers to manage their inventory more effectively, improving the shopping experience for customers and enhancing supply chain operations.

For a food delivery service like DoorDash, accurate product data is essential to ensuring customers receive the items they expect. DoorDash uses LLMs to process raw merchant SKU data, identifying and tagging product attributes automatically. This improves search relevance for customers looking for specific items and aids delivery drivers in efficiently locating and retrieving the right products. 

The good part is that Product Attribute Extraction (PAE) can be applied across other industries as well to streamline operations and improve customer experiences. In the automotive sector, PAE can help extract vital vehicle specifications and features from product listings, improving searchability and comparison tools for buyers. In real estate, it can automatically identify key property features, such as square footage, number of rooms and amenities to simplify the property listing process.

AI-Powered Code Suggestions and Autocompletion

Software development can be complex but GitHub’s Copilot, powered by LLM is changing how developers write code. Copilot assists programmers by providing intelligent code suggestions and autocompletion, significantly enhancing coding efficiency. By learning from vast repositories of open-source code, the AI assistant offers developers real-time assistance, reducing development time while improving code accuracy. 

Beyond code suggestions, LLMs can help with automated bug detection, identifying potential vulnerabilities and possible fixes in real-time. They can also assist in code documentation, generating comments or explanations to make complex code more understandable. LLMs can improve software testing by suggesting test cases based on code logic or identifying edge cases. This reduces cognitive load, speeds up development and increases overall software quality.

Transaction Query Automation

Digits has improved accounting efficiency by using Large Language Models (LLMs) to suggest queries related to banking transactions. When accountants review transactions, the system leverages LLMs to generate relevant questions for clients, streamlining communication and reducing manual effort. Accountants can confirm, modify or refine these suggestions before sending them for accuracy and relevance. This approach not only saves time but also improves the quality of client interactions. By incorporating LLMs, Digits automates routine tasks, freeing up accountants to focus on more complex financial management tasks. 

Get Started with LLMs on Hyperstack 

Looking to automate queries like Mercado Libre or scale product catalogue management like Walmart? We provide on-demand access to powerful GPUs like the NVIDIA H100 SXM and the NVIDIA H100 PCIe, ideal for handling the computational demands of LLMs. Hyperstack is where you build market-ready products. From development to deployment, our platform offers everything you need to move from innovation to execution. Our new NVIDIA H100 SXM systems are ready to power your next LLM breakthrough. 

Deploy NVIDIA H100 SXM in Minutes!

With our easy 1-click deployment, flexible configurations and on-demand availability, your LLM workloads can be running in minutes. Ready to get started?

But that’s not all, we also offer an LLM Inference Toolkit, designed to streamline deployment, simplify LLM management and optimise model performance in real time. It supports open-source LLMs with flexible deployment options for local or cloud setups. Want to get started? Check out our guide below!

Get Started with the Hyperstack LLM Inference Toolkit

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FAQs

What are Large Language Models (LLMs)? 

LLMs are AI models that can understand, generate, and respond to human-like text, enabling businesses to automate various tasks like customer support and content generation.

How can LLMs improve customer support?

LLMs can automate responses to common customer queries, providing faster and more accurate support, and enhancing the overall customer experience.

How do LLMs help in handling technical queries in businesses?

LLMs can automate responses to technical questions, streamlining workflows and boosting productivity.

Can LLMs be used for data extraction from large product catalogues?

Yes, LLMs can automate the extraction of key product attributes from large catalogues, improving inventory management and search relevance for retailers.

Can LLMs assist in software development? 

Yes, LLMs can provide AI-powered code suggestions, auto-completion, bug detection, and code documentation, speeding up development and improving code quality.

How quickly can I deploy LLM models on Hyperstack? 

With Hyperstack's simple 1-click deployment, you can start running LLM workloads in just minutes, saving time on setup and configuration.

How can Hyperstack support my LLM projects? 

Hyperstack offers on-demand access to powerful GPUs like NVIDIA H100, ideal for handling the heavy computational demands of LLM workloads, with easy 1-click deployment.