10 best large language model use cases for business

10 best large language model use cases for business

Whether it comes to a new software launch or updating your existing product, in-depth market research will be a part of any company’s journey. In the past, this process involved manually sorting out documents and files like focus group reports, end-user surveys, and competitor analysis findings — no wonder it would have taken months to be completed.

However, not anymore — the evolution of large language models (LLMs) is the culprit. While the NLP market size signifies its rapid evolution — $63.37 billion by 2030, the LLM market has proven to be its crucial part — around $260 million by 2030 with a mind-blowing CAGR of over 140%.

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Although understanding how to build large language models will let you set your trends in the target industry, it is better to get actionable insights from worldwide LLM use cases first. 

Stay tuned to find a perfect path to rapid innovation to boost your business.

What are large language models?

Large language models (LLMs) are algorithms that advance natural language processing technology and unsupervised learning to interpret data and provide the desired results. 

The origins of contemporary AI large language models, which serve business purposes like service monetization or project scaling, can be traced back to pioneer algorithms such as ELIZA — one of the first LMs, which debuted in 1966 at the Massachusetts Institute of Technology. Throughout its short history, large language models have undergone numerous modifications and are now capable of using thousands of parameters instantly.

How do large language models work? Its core relies on the training data (divergent textual sources to form its knowledge base), transformers, parameter tuning, attention, preprocessing, and tokenization mechanisms. Once the input encoding is over, such a system utilizes several tools it possesses to process the obtained data for further text generation. Request after request, LLMs keep their constant training.

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With large language models explained, now is the time to get to know more about popular LLM use cases and how you can utilize their potential in practice.

Types of large language models

Before you consider custom AI development and envision your own large language models examples, it is essential to distinguish LLMs from one another and know the answer to “What are LLMs good at?”

In general, there are two categories of AI large language models — open-source and closed-source ones. The difference between them is simple — it is about whether their source code is publicly accessible or not. While open-source products follow collective advancement and modification efforts, alternatives are commercial by nature in most cases.

If you think that is enough for preparations before learning how to make a large language model, there are more open-source large language model types to explore:

  • General-purpose — as the name implies, these are standard solutions that can perform numerous tasks from different spheres and are trained on publicly accessible texts on the Internet.
  • Multilingual — such LLMs will support a lot of languages and will showcase their value for completing cross-lingual assignments.
  • Task-specific — like DeepL for online translation, there are several more models tailored to succeed in a particular area.
  • Domain-specific — contrary to general-purpose systems, these models will assist in certain fields better. They are intentionally trained on websites focused on healthcare and finance so that more in-depth answers can be obtained.
  • Few-shot — such models don’t have to rely on hundreds of background files to provide excellent results. They are deliberately established as those trained on small amounts of information.

Comparative analysis of leading LLMs

The digital transformation for LLM companies can be custom, but a lot choose to see how popular LLM types can modify their operational capacity and workflow. In the table below, let’s see how large language models work in general.

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LLM use cases in business

No matter what large language models examples you consider, their services can be sufficient for any system and workflow. That’s what makes their services so sought-after and advantageous for businesses of any caliber. 

What are some of the applications of LLMs? There are LLM companies in fashion, human resources, military, finance, healthcare, social media, and other industries. With our IT consulting services, you can implement those strategies in practice.

How AI large language models can simplify your operational processes and boost your performance? Here are several examples:

  1. Start by integrating a custom LLM into a search engine of your choice, including Google. This strategy will come in handy to reveal hidden customer intents behind their queries, which can help businesses tune their services and keyword portfolios accordingly. Google’s Bard stands behind several LLM use cases in the area, providing personalized content recommendations, in-depth search history analysis, and other reports to get a compelling edge for your project.
  1. To simplify your software development processes, opt for large language models tailored to debugging, reviewing, and writing codes in different programming languages. All you need is to make a command for it to be completed in minutes or even seconds. ChatGPT and StarCoder are great options to consider for beginners.
  1. With the help of black-box tuning for language-model-as-a-service, you can optimize the performance of AI models without understanding or altering the underlying algorithms. This approach lets you customize AI services to meet unique business requirements and enhance the efficiency of integrating these models into existing workflows. 

At COAX, we follow your company’s demands and design an algorithm to complete task-specific prompts and let you outperform your competitors in terms of optimization, versatility, and adaptability.


Keep on reading to get to know more impressive LLM use cases to diversify your business suite of tools and technologies. Let’s dig in!

1. Language translation

Compared to human translation, AI-empowered LLMs might not be as effective for literary translation — they might lack text consistency and cohesion in this case. Nonetheless, it doesn’t mean this technology is useless:

  • For startups, such applications are game-changing. They don’t require to allocate lots of resources to professionally translate their website’s page. Without wasting time, they can present their platforms in any language with the means of LLM-based automated translation.
  • These mechanisms are gaining momentum across the markets. It is a nice option to maintain connections with partners and allies in the military field and get live translations of media from English to Chinese or Arabic, for instance.

Universal LLMs like ChatGPT can perform this task, but it is better to choose special large language models to cope with it. Such LLM use cases would be troublesome without DeepL, Google Translate, Phrase (former Memsource), and Bing Microsoft Translator.

2. Content creation

Whether you are interested in the automated generation of social media posts or articles, modern large language models will prove their worth. In such a way, you can upgrade your content strategy and obtain a nice source of relevant data and details. Please note that this tactic requires an in-depth understanding of how Google and other search engines rank AI-generated pages so as not to let your websites down.

It is better to start by implementing these tools for preparing creative text formats. For example, they will help you avoid the hustle and bustle of creating code or metadata for your pages. Aside from viral ChatGPT, you can also use multifunctional platforms like Serpstat with AI tools for simple content generation.

3. SEO

If you don’t work on boosting your platform’s ranking in SERPs, your business’s ROIs might be quite lacking, and so might your brand recognition and online visibility. LLM use cases prove how efficient such instruments are in improving a website’s structure and content, as well as optimizing it for search engine indexing and crawling. You can drive more organic traffic to your system by making LLMs answer end-user queries and elevating customer satisfaction and user experience quality overall.

4. Content moderation

With systems like Grammarly, you can edit and cross-check the informational and stylistic functionality of your texts and avoid any offensive, inappropriate, or outdated content from your pages. You can use LLM’s prompts to generate human-like comments on your posts and ask for the system’s opinion on your post’s message and efficiency. Such solutions will benefit businesses with unique insights and creative perspectives.

5. Customer service

One of the modern requirements to be attractive in the eyes of audiences is to be available 24/7. While it may be challenging to hire an in-house customer care team to work within those hours, the use of custom-made or trusted chatbots and virtual assistants will come in handy. Features like context awareness, language support, and natural language processing let chatbots like Lyro be proficient in real-time customer service.

6. Sentiment analysis

Contemporary large language models have a thorough awareness of language context and different meanings and stylistic nuances. Not only are they trained on huge datasets to handle tons of information in a matter of seconds, but also large language models can self-improve without difficulty. 

Ranging from consumer evaluations of your business to social media content, these networks are capable of effectively predicting the sentiment behind fancy words and phrases. Grammarly and Innodata are nice solutions to discover your custom LLM use cases.

7. Virtual assistants

Chatbots and virtual assistants are excellent examples of LLM black box models. With their help, your company can enhance the overall service’s accessibility, increase user engagement through instant-facilitative conversations, provide accurate information about your audience, and perform different tasks. Google Assistant and Alexa are stunning illustrations of the general LLM capacity.

8. Personalized marketing

Top LLM companies apply these algorithms to achieve the following:

  • Create custom email campaigns tailored to the target business needs and audience’s demographics and preferences
  • Analyze the efficiency of marketing projects, including measuring social media activity changes before and after campaigns
  • Customize newsletters with personalized suggestions for services and products

9. Fraud detection and prevention

There is an extensive list of large language models that have tremendous capabilities for evaluating textual data, discovering hidden trends and messages, as well as locating any abnormalities. That’s why they can be useful in supply chain management in any industry and risk assessment and mitigation. 

LLMs can spot any informational inconsistency and avoid any issues associated with counterfeit products — a must-have for enterprises, especially given its ability to monitor large volumes of incoming and outgoing data in real-time. 

By adjusting the system’s push notifications and alerts, you will be able to take action instantly and prevent reputation damage and financial losses. We develop custom AI language models that act as a shield for your travel app and minimize financial fraud in the field.

10. Cybersecurity

Large language model companies will take the most out of advanced features provided by modern LLM-based products:

  • Data augmentation for improving fraudulent activity detection speed and accuracy
  • Data cleaning for maintaining the system’s self-healing and self-training capacity
  • Tokenization and weight adjustment for setting analysis standards for the model
  • AI verification and validation for achieving higher standards of data quality and metrics

One of the solutions to protect databases from information leakage and harmful content implementation is LLM Guard. It assists interested parties in maintaining your platform’s health and automating site audits and anti-fraud scans.

8 things about LLMs you should know

With so many large language models examples, it might be tricky to notice how many similarities they actually have. Let’s check what features and characteristics ensure the universal applicability of these algorithms to LLM companies. Here are eight things to know about large language models:

  1. Don’t rely on the efficiency of brief interactions with LLMs — they won’t protect you from their two-faced nature and can be misleading.
  2. Ideally, LLMs shouldn’t become the source of propaganda and offensive content, staying aside from expressing the views and opinions of their founders.
  3. In the future, LLMs can replace even more jobs and outperform people in solving problems and analyzing large volumes of data.
  4. LLMs are black-box mechanisms with somewhat unknown inner procedures and operational processes.
  5. At the moment, there is no uniform methodology to analyze their global network and supervise such products.
  6. Self-training and self-healing are possible for LLMs.
  7. LLMs can discover new features and specific skills, the higher the potential training value and intensity. The results are uncontrollable and unpredictable, though.
  8. On the other hand, large language models are guaranteed to enhance their functionality and value, the more top-notch training they get.
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LLMs trends and future directions

Now that you know the answer to “What is large language model (LLM)?”, it will be easier to check your prediction skills and see what awaits AI language models in five or more years.

When analyzing LLM use cases, entrepreneurs commonly hesitate about the usability of large language models (LLMs) in the long run. The problem is that there are several unsolved issues such mechanisms may create:

  • AI ethics — the system’s quality and data interpretation capacity will depend on how it is trained. There are cases when LLMs are based on information from multiple sources without taking into account copyright laws. It might be a challenge for those interested in automated content generation.
  • No “out of the box” performance — if you want to join other LLM companies, you have to remember about prospective risks and challenges of large language models. Generic AI platforms may not correspond to your brand’s quality requirements and domain complexity. In this case, creating a high-accuracy and sophisticated application may be the answer.
  • Two-faced” models — given that AI language models are a pretty recent discovery, more research is yet to come. According to the study, once deployed, LLMs may behave improperly and learn to imitate accurate information through poisoned data. Open-source systems are commonly biased and may put your data privacy at risk.

In the future, if these and other LLM-related issues are solved successfully, further technological integration won’t be surprising. VR, AI, and LLM can become a jaw-dropping combination, don’t you think? Anyway, the interest in such algorithms is unlikely to fade away — more large language systems to handle specific tasks in different industries as a result.

Get the most out of LLMs with COAX

As you can see from the LLM use cases above, the chosen path how to train a large language model will predetermine its functionality. The combination of technologies, including blockchain, can bring more powerful results to the ground and assist businesses in surpassing their current limitations. 

At COAX, we ensure your brand will take the most out of custom large language models tailored to your specific requirements and needs. Throughout the discovery phase and in-depth data analysis, our team will deliver an AI-empowered product that will handle huge volumes of information and transform your weaknesses into strengths. Get a free quote for your personalized LLM now!

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