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large language models applications How to Develop and Leverage Custom LLMs for Business: Benefits, Use Cases, and Tips
AI

How to Develop and Leverage Custom LLMs for Business: Benefits, Use Cases, and Tips

Jan 31, 2025

17 mins read

From a promising trend, artificial intelligence (AI) has turned into a must-have implementation across almost all industries with 72% of businesses adopting AI for at least one function. Many companies start AI implementation with large language models (LLMs). 

Today, we are ready to share some insights about custom LLMs and their value for businesses. This article explores the benefits and use cases of custom LLM development and provides some tips on how to leverage large language models efficiently.

What is a Custom LLM?

Large language models (LLMs) are machine learning models capable of comprehending and generating human language text by analyzing massive data sets. Their popularity has been booming in recent years with a global large language model market projected to grow at a compound annual growth rate of 35.9% from 2024 to 2030. 

what are some of the applications of llms?
Important facts on AI/LLM implementation

Some of the most popular LLM examples include GPT-4o, Google Gemini, and Meta’s Llama. There are also some examples of large language models available in the Azure OpenAI Service. You can check out our overview of these LLMs in our article dedicated to the topic of corporate LLMs. 

All these solutions, as well as other popular LLMs and generative AI platforms, can serve as a foundation for corporate LLM. A corporate LLM is a custom large language model using the knowledge base of a particular company and serving its goals, whether it goes about enhancing internal operations or improving communication with customers. To build such an LLM you can use pre-built templates, algorithms, and LLM configurations. For example, Azure AI services offer a great variety of pre-built components for LLM development.

In one of our recent articles, Leobit’s CEO, Oleksa Stelmakh, has shared his experience with custom LLMs, their development opportunities and challenges, and value such solutions brought to Leobit.

An LLM uses deep learning – a process that involves the probabilistic analysis of unstructured data. This analysis enables the deep learning model to recognize the distinctions between pieces of content without human intervention. The more relevant information LLM learns, the better its ability to respond to unpredictable queries.

Benefits of Using a Custom LLM for Your Business

In fact, LLM usage can bring a business a variety of benefits. Here we include some of the most common advantages of LLM implementation

large language models applications
Benefits of corporate LLMs

Improved workflow efficiency

LLMs can assist human employees handle many routine processes, by accumulating data and handling some tasks, such as text translation and documentation formatting. 

This feature can boost the efficiency of your teams dramatically by reducing human involvement and providing automation for routine tasks. This, in turn, allows your employees to save time and focus on business-critical activities, boosting your business efficiency and productivity.

Enhanced customer experience

Approximately 44% of businesses say they adopt AI to enhance the customer experience. In particular, LLMs help companies improve their client interactions by speeding up the customer support response time, providing personalized content to the users. LLM capabilities can empower chatbots and virtual assistants to provide informative and personalized responses to users based on the information they have learned. By handling some basic customer queries, LLMs reduce the loads of customer support specialists, allowing them to focus on more complex customer requests and provide better responses on time. 

A great example of an LLM-based user assistant is Leo, Leobit’s AI sales email auto-response solution that provides fast and personalized responses to customer emails and website form submissions. Leo also qualifies and scores leads based on request type, industry, and technology match, allowing our sales team to ensure custom workflows corresponding to lead categories. 

AI can also enhance the time required to process user files, such as documents and photos. In addition, large language models are commonly used for optimizing an app’s or a website’s search engine, which enhances the user’s chances of finding the right content with very approximate requests.

In sum, all these benefits boost customer satisfaction, which positively impacts your market reputation, improves user retention, and attracts new users to your app or website.

Advanced decision-making

LLMs provide additional support to business decision-makers aiming to get and weaponize relevant data. Such models can revolutionize data analytics by efficiently processing and interpreting large volumes of unstructured data, extracting key insights, and presenting them clearly. They make data analysis more accessible to non-technical users through natural language queries, delivering intuitive responses and visual summaries. LLMs can also assist different specialists in identifying trends, detecting anomalies, and preparing predictive models.

No wonder that global technology giants employ LLMs for analytics. In particular, Google uses LLMs to analyze search results, find search trends, and analyze user behaviors. This information helps Google to provide better user search results, receive, and process vast amounts of valuable data. 

Knowledge base for employees and customers

Large language models help you create a consistent company knowledge base useful to your team. Best practices and professional tips, guidelines, lists of corporate rules, and relevant company information can assist specialists across different departments. LLMs provide employees with simple and fast access to the necessary company data. An example of such a solution is Leonardo, Leobit’s AI-based HR assistant that provides answers to the employees’ FAQs by retrieving the relevant information from the company’s curated knowledge base. 

In addition, LLMs can help businesses enhance their content generation. According to Forbes, over 90% of marketers use content as a vital part of their marketing strategies. LLMs can assist marketing specialists in creating content for websites, social media, and blogs. By feeding quality information to your large language model, you can ensure that such a model generates informative texts that provide useful insights to your users, enhancing their satisfaction and ensuring your company’s market presence. 

Rich potential for continuous innovation

Apart from helping to analyze and model the impact of industry trends, an LLM can support idea generation and product development. It can suggest improvements and generate prototypes based on user feedback and market analysis. The potential for improving your LLM solutions is truly vast, as long as you can unlock new features and use cases for AI algorithms over time. Mind that a large language model can degrade over time as well. Therefore, it is important to constantly monitor and upgrade them, train a model once significant changes occur in the company, and stay committed to continuous improvement. 

Common LLM Use Cases for Business

There might be a great variety of use cases for corporate LLMs, depending on your business needs, data you use, and your model development, configuration, and training approaches. Here, we highlight the most common LLM use cases adopted by businesses across various industries.

User assistance

Customer communication is one of the most common use cases for LLMs. Such solutions can assist customer support specialists by analyzing user queries, generating contextually relevant responses, and adapting based on interaction history. They use relevant data to understand nuances, intent, and context, enabling more natural and informative conversations. For example, we at Leobit use Leora, a vocalized AI sales assistant that uses the company’s knowledge base to provide detailed and personalized answers to prospects. The solution effectively takes on the role of a sales representative, saving clients time by providing immediate customized responses. Ongoing LLM training and proper data organization helps you improve your model’s ability to manage diverse topics and provide consistent personalized assistance to your clients. 

Content generation and summarization

LLMs can assist marketing teams and other employees with content generation by creating texts and templates for different purposes. For example, you can use an LLM to generate a structure for a marketing blog post or generate social media posts using their machine learning capabilities. The key point is to insert correct prompts and ensure that your LLM knows all the required information. Your teams can also use large language models to summarize texts of different length and complexity. As a result, employees can save much time, while delivering and operating with relevant LLM-generated texts. 

Language translation

Many businesses use large language models to translate and interpret texts across different languages. For example, you can use it to translate documentation or instructions for a particular product. LLMs are capable of analyzing texts, capturing nuances, context, and idiomatic expressions. They use vast multilingual data to provide accurate translations that preserve meaning, tone, and style, making content accessible to diverse audiences. You can configure and train LLMs to work with complex language pairs, regional dialects, and culturally specific expressions. 

Information retrieval and analytics

A custom LLM can help to collect the necessary information by processing user queries and extracting specific information from vast datasets. As a result, you can get important market information for BI tools. Moreover, AI algorithms can analyze and synthesize data, identifying patterns and key insights that support decision-making. Properly-tuned LLMs can quickly organize and sort big loads of complex information, providing actionable insights on market trends, customer requests, and common challenges in different industries. For example, Leobit uses Leona, an AI-based employee, to monitor outsourcing platforms, analyze and categorize cooperation opportunities, and generate and submit project proposals automatically. 

Leona
Leona, Leobit’s proposa; generation and bidding solution

Assistance in coding

According to McKinsey, AI can enhance the speed of writing the code by 35%-45%. This leads to a significant reduction in time-to-market for software development projects. LLMs can assist software developers in coding by generating code snippets and functions based on user instructions. Technical specialists can also use LLMs in software debugging because AI algorithms are capable of analyzing error messages and suggesting solutions. In addition, a properly-configured LLM can assist software engineers in code refactoring or optimization. Trained to support multiple programming languages and frameworks, LLMs help software developers improve productivity and overcome coding challenges much faster. 

Data classification and organization

Many businesses use LLMs to enhance data classification. Large language models are capable of categorizing unstructured data into meaningful labels, which makes information easier to analyze and retrieve. You may configure LLMs to identify patterns, themes, or sentiments within datasets, which enables more accurate organization of data based on context and intent. With LLMs, teams can enhance the efficiency of data organization workflows and achieve high levels of smart automation in data management. For example, Leo, our email auto-response solution, processes customer requests to ensure more efficient lead categorization and scoring based on request type, industry, and technologies in which the lead is interested. 

LLM PoC Architecture
Leo’s proof of concept architecture

LLM Implementation in Combination with other Tools

There are also certain use cases when custom LLMs can be efficiently applied in combination with other tools and solutions to fulfill particular business workflows. Here are some notable examples of such cases.

Text-to-image

LLMs are often applied in combination with image generation solutions to enhance text-to-image translation capabilities. LLMs can generate detailed and precise prompts by synthesizing the provided texts. These prompts are used by image generation tools to create visual representations, enabling users to generate illustrations for their content or visualize product concepts. A well-trained LLM understands context, themes, and subtle nuances in the text to create accurate and relevant image-generation prompts. The entire process can be automated through the integration of an LLM with an image generation solution so that the provided text is used to generate a prompt which is used for generating an image without human involvement. Such an integration significantly reduces the time required for creating illustrations for marketing materials, concept visualizations, and other visual support for the texts. For example, you can use such an integration to generate images that will enhance the content on your company’s website.

Image-to-text

LLMs are often used in combination with computer vision to extract valuable information from visual content. LLMs can integrate with computer vision algorithms that capture particular features of images. A large language model then can use the takeaways provided by computer vision tools and generate their detailed, contextually accurate descriptions. The entire process can be automated, which saves time for users or specialists working with such tools. Companies can use such a combination to summarize documents or enhance their analysis. In addition, such a solution is often used for getting quick textual interpretations of complex schemes, product descriptions for e-commerce platforms, or basic descriptions for illustrations in books or presentations.

Text-to-speech

Text-to-speech (TTS) solutions powered by LLMs are often used to convert written content into natural-sounding speech. Businesses combine large language models with speech synthesis technologies to generate realistic and expressive audio with human-like intonation and emotions. This functionality can be used in virtual assistants on different websites as an accessibility feature or a certain way to enhance user convenience. In addition, LLM-based text-to-speech functionality is widely applied for developing audioguides or audiobooks.

Why to Outsource the Development of Custom LLMs?

Even the world’s leading LLM-based AI services can provide false or unverifiable responses, known as hallucinations. For example, a 2023 study revealed that ChatGPT was generating hallucinated content in 19.5% of responses. The point is that developing a custom LLM can be challenging because mistakes in data curation and organization, along with a lack of understanding of proper tuning practices, can significantly impact the performance and reliability of the model. Failure to handle these tasks properly can lead to LLM hallucinations and make such a model ineffective for your businesses, regardless of the purpose you are using it. There are quite many aspects to track while building a corporate LLM, ranging from keeping your data quality high to establishing and maintaining the system security.

A practical solution for such challenges is outsourcing the development of a custom LLM to a team with a strong practical experience with large language models. In this case, you will gain extra expertise for your LLM development project and get an ability to focus on your business goals, while the technical aspects are handled by a team of specialists.In addition to saving time and costs, this decision will ensure you receive a secure, reliable tool that delivers value to your business.

Tips on Developing Custom LLMs that Bring Value to Business

Recent studies show a growing distrust towards generative AI. In particular, in 2023, 21% of millennials distrusted the information provided by generative AI, while in 2024, this figure rose to 30%. Over the same period, the distrust towards generative AI has grown from 24% to 32% among baby boomers. The point is that machine learning models, including LLMs, are often exposed to poor data and hallucinations that decrease their efficiency and the quality of information they provide drastically.

There are certain ways to help companies keep their LLMs in the right state and constantly evolving. Here are some tips that help to maximize the value of LLMs for business.

Build custom LLMs on the right and properly-categorized data

Around 53% of business executives state that data quality is one of the biggest concerns in AI adoption while 49% also express concerns about data silos. To avoid general and unrelated outputs from LLMs, it is important to ensure that it uses the right information. This means building an LLM on curated data, which is selected, reviewed, and approved by your team. For example, you can prepare a solid list of documentation, as well as information from your corporate website to input it into your LLM. As a result, the model will provide its outputs based upon such data. 

To provide responses tailored to different requests and the needs of diverse user categories, it is fundamental to ensure the right approach to data categorization. In particular, while working on our LLM-based email auto-response solution, the Leobit team used the following categories of information:

  • Company website(s)
  • Project cases with all related information
  • Internal presentations
  • Legal documents
  • Internal knowledge base (e.g., Confluence, wikis, OneDrive, Google Drive)
  • Emails
  • Recorded calls
  • Graphic objects like charts, images, and diagrams.

Focus on LLM fine-tuning and prompt-tuning

To reduce the threat of system hallucinations, embrace the best tuning practices for your AI system. The most common options are fine-tuning and prompt-tuning.

  • Fine-tuning involves adapting large language models to perform specific tasks or follow particular styles by training them on additional, task-specific data. The specialists take a pre-trained model (with general language knowledge) and further tailor it to understand and generate content relevant to the new dataset. The core principle of fine-tuning is the rigorous categorization of data used for deep learning with prioritized data used as the main asset for model training. Common tuning approaches are domain-adaptive pre-training techniques and task-specific fine-tuning.
  • Prompt-tuning is, typically, a less comprehensive approach to large language model training than fine-tuning. Instead of boosting the model’s analytics by using curated data, this approach is primarily centered around specific scripts and keywords. The key benefit of prompt-tuning is that it can be completed faster and is less resource-intensive than fine-tuning. Instead of retraining the model on new data, prompt-tuning involves designing precise instructions or context cues that improve the model’s performance on desired tasks.

Regardless of the approach you choose, we suggest you start applying LLMs in areas where certain levels of biases and hallucinations can be tolerated. The point is that attempts to fully eliminate them from the start may be inefficient.

Run regular security checks

To keep an LLMs secure, it is important to have regular security audits and vulnerability assessments that explore the entire system built around an LLM. This will help safeguard the solution against vulnerabilities such as data leakage, prompt injection, and adversarial attacks, threatening user data and the solution’s performance. A security audit may help to identify potential weaknesses in system architecture, data handling, and response generation. It also ensures compliance with the industry’s standards and regulations. During such a security check, specialists will primarily focus on examining the data protection practices, LLM integration points, and monitoring the system for abnormal behavior. Apart from running a security check, you can mitigate the risks with the help of regular system updates and patches.

Monitor the performance of your LLMs regularly

A recent study reveals that up to 91% of AI models degrade over time. This means that apart from keeping an LLM secure, it is also vital to monitor its performance and keep the data that the model uses updated and relevant. Introduce constant performance monitoring, data quality checks, and recalibration of models. This will help you address any drift or degradation in large language model accuracy. When it comes to monitoring an LLM, it is important to establish a system of metrics for evaluating its efficiency and quality of the model’s outputs. For example, you can focus on such metrics as the BLEU score and ROUGE, as well as on text similarity metrics, semantic similarity metrics, and reference-free metrics.

Conclusions

AI has become essential in the modern business environment as many companies, including Leobit, use LLMs to enhance their processes. Custom LLMs offer benefits such as improved workflow efficiency, enhanced customer experience, support for data-driven decisions, a quality information base for both customers and company employees, and opportunities for continuous innovation. Common large language models use cases include chatbots,content creation, analytics, translation, data organization, and coding assistance. Tried-and-true methods to ensure the efficiency and business value of LLMs include focusing on quality data, conducting regular model tuning, performing security audits, and monitoring performance.

Implementing a corporate LLM and keeping track of its development might be a challenging, as well as time- and resource-consuming, task. Leobit, a .NET, AI, and web application development company certainly has such an experience. We possess a deep knowledge of AI technologies, an extensive portfolio of successful AI-based projects, and are actively implementing AI-based solutions to enhance our workflows and operations. 

Contact us to build a custom LLM that brings real value to your business. 

 

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Artem Matsa | Business Development Director