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The most common LLM applications we see for business value

Most users barely scratch the surface of what LLMs can do.
Since ChatGPT captured the public’s attention in late 2022, millions have been tinkering with large language model (LLM) applications. Use cases range from fun (creating fan fiction) to functional (drafting email replies).
But most users barely scratch the surface of what LLMs can do.
“LLMs are fun to play with, but their potential is far greater than most people realize,” says Donovan Crewe, Senior Software Architect at Lumenalta. “Companies on the bleeding edge know this and are implementing many under-the-radar LLM applications that are fundamentally transforming how they operate.”
We talk to these companies every day and want to pull back the curtain to reveal some common large language model use cases that you can apply to your business.

5 powerful large language model applications

1. Streamlining legal document analysis

When you think about a lawyer’s day-to-day, courtroom battles and persuasive arguments probably come to mind. That may be true for senior lawyers, but for juniors, it’s much less glamorous — more like hours and hours spent reading through legalese.
Late nights spent poring over legal documents is a rite of passage in the field, but younger lawyers can add significantly more value by delegating document analysis to a robot.
LLMs are perfectly suited to detail-oriented tasks like these. Law firms and corporate legal departments are leveraging these models to automate the assembly of basic documents such as contracts, non-disclosure agreements (NDAs), and wills.
These AI-powered tools can also take user input, such as specific client needs and relevant legal precedents, to generate customized documents that meet precise requirements.
The result? With less time eaten up by document creation, law firms can take on more clients. In-house legal teams can turn around internal requests faster and with fewer errors. And more lawyers can get home before dinner.
Related: Top 3 metrics of generative AI in business

2. AI-powered sales negotiation support

A strong dealmaker isn’t made overnight. Interpersonal skills, a nuanced understanding of the market, and negotiating tactics take years to hone and won’t be replaced by a robot anytime soon. 
But LLMs can still be a potent tool in a salesperson’s arsenal, providing data that gives them the upper hand during negotiations.
They can ingest and analyze vast datasets, including historical sales, competitor pricing models, and even customer sentiment gleaned from social media platforms.
Insights that would take a sales team weeks to uncover can be identified in a matter of minutes.
Let’s illustrate this concept with an example. Sarah is negotiating a software licensing contract with a rapidly growing startup. After reading through their marketing materials, she notes that scalability and cost efficiency are top priorities for this client. But pricing discussions have reached an impasse.
Here’s where AI enters the picture: in real time, the LLM analyzes Sarah’s CRM data, revealing that the startup’s previous software purchases all prioritized cost-effectiveness. With this in mind, the model recommends that Sarah propose a tiered pricing model that would offer a lower initial subscription fee with the option to scale up at a discounted rate as the startup grows.
To support Sarah further, the LLM scans historical data on similar deals with tiered pricing models, predicting a significantly higher chance of closing the deal with this approach. After presenting the tiered pricing model to the client, they agreed that it aligns well with their priorities, and she secured the contract.
This hypothetical example demonstrates how salespeople are leveraging LLMs to boost the top line, shorten sales cycles, and improve their productivity.

3. Optimizing software development through assisted code creation and review

Like lawyers, many software developers spend the bulk of their time on unsexy work, such as writing boilerplate code or meticulously debugging errors.
While many would prefer to spend their time coming up with solutions to complicated software problems, these tedious tasks are just part of the job.
Until LLMs came into being, that is. These AI models aren’t yet able to create complex software applications, but they’re already making developers more productive by enabling the offloading of routine tasks.
LLMs can be trained to generate code snippets based on natural language instructions.
Think about a developer needing to implement a common data sorting function. Instead of manually writing each line of code, the developer can describe the desired functionality in plain English. The LLM can then generate the corresponding code snippet, saving the developer hours of coding time and effort.
Beyond code generation, LLMs can also review existing code for potential bugs and security vulnerabilities. All developers have gone through the painstaking process of meticulously sifting through code to find a bug.
LLMs can analyze code structure and logic to identify bugs and pinpoint potential security risks that might be missed by the human eye.
Related: 7 ways AI boosts your software QA
These capabilities are saving developers lots of time, but assisted code creation brings a host of benefits to businesses as well:
  • Faster time to market: A streamlined development process translates to quicker product launches.
  • Reduced development costs: Automating repetitive tasks and minimizing errors can lead to significant cost savings.
  • Improved software quality: LLM-assisted code review leads to fewer bugs and vulnerabilities, enhancing overall software quality and the user experience.

4. Predicting market trends with unprecedented accuracy

Consumer-facing industry veterans will tell you that predicting market trends is an informed guessing game at best and a shot in the dark at worst.
Historically, there have been two main hurdles preventing businesses from being able to predict where trends are headed: a lack of data and the tools to interpret that data at scale.
“At this point, plenty of consumer-facing businesses, like retail, have the data part figured out,” says Crewe. “Social media is rife with feedback on every consumer trend imaginable — the challenge is distilling it down into actionable insights, which is where LLMs shine.”
Churning through massive amounts of data and uncovering hidden patterns within it is one of AI’s biggest strengths. And it’s transforming market trend forecasting as we know it.
Take the athletic apparel industry, for example. Fashion brands have traditionally relied on seasonal trend reports and focus groups to anticipate what’s around the corner, which tends to result in missed opportunities and excess inventory.
LLMs are revolutionizing this approach through techniques like scanning the tone and content of social media discussions. For instance, a surge in social media mentions of HIIT workouts could signal a growing consumer demand for functional and supportive athletic wear, which retailers could leverage for inventory planning.

5. Crafting compelling content at scale

High-quality content is a great way to establish yourself as an industry thought leader and capitalize on search terms, but it can be a time-consuming process. While subject matter experts (SMEs) and product marketers are the ideal authors for blogs, newsletters, and product pages, enlisting them to create content diverts attention from their other responsibilities.
LLMs can’t yet match the abilities of a strong writer, but they can get pretty close. SMEs can feed the model background knowledge about your industry, offerings, and target audience, then ask it to write a brand-aligned blog post. The blog it produces will probably need some work, but it’s much more efficient than starting with a blank document.
These models are great brainstorming partners, too. If you’re stuck on an idea, ask an LLM to peruse industry trends, competitor content, and audience searches to generate innovative topics that resonate with your target market. Many writers find this collaborative approach much easier than doing all the research themselves.
Ultimately, LLM-assisted content creation is a boon for businesses. Marketing teams can boost their search engine presence by churning out more content without needing additional resources like freelancers. They also reduce editing costs by automatically checking for grammar and factual accuracy.
Relevant case study: See how Domino pioneered shoppable content.

The future is LLM-powered

These are just a few examples of the many LLM applications transforming how work gets done. As large language model architecture improves, we can expect even more groundbreaking use cases to emerge.
But while most LLMs are easy to use, implementing them can be a minefield. Besides ensuring they generate real business value, data, privacy, and other concerns can be a headache for business leaders.
The seasoned pros at Lumenalta have decades of experience helping businesses implement tailor-made software solutions. From deep tech firms to retailers, we’re your ideal partner for navigating the LLM-powered future.

Learn more about LLM applications for your business.