12 Practical Large Language Model (LLM) Applications

In fact, researchers estimate that generative AI will become a market of by 2032 1.3 trillion dollars as more users experiment with generative AI solutions such as ChatGPT, Google Bard and Bing Chat.

However, as the technology is still in its infancy, the use cases for LLMs in companies have yet to be defined.

At first glance, LLMs can be used in any scenario where a company needs to analyze, process, summarize, rewrite, edit, transcribe, or derive insights from a data set or input text. As they become more widespread, there are some practical applications of language models that appear to be promising.

12 Best LLM Applications

An overview of the best LLM applications 2023, including an explanation of the functions and possible uses.

1. Translation with language models

One of the simplest practical applications for LLMs is the translation of written texts. A user can enter a text into a chatbot and ask it to translate it into another language and the solution will automatically start translating the text.

Some studies have found that LLMs like GPT-4 are competitive compared to commercial translation products like Google Translate. However, the researchers also note that GPT-4 is most effective when translating European languages, while it is not as accurate when translating “poor” or “distant” languages.

2. Malware analysis

The launch of Google’s cybersecurity LLM SecPaLM in April 2023 demonstrated an interesting use of language models for malware analysis. The Google VirusTotal Code Insight, for example, uses Sec-PaLM LLM to scan and explain the behavior of scripts to tell the user whether they are malicious or not.

Scanning files for malware in this way means human users don’t have to sandbox them to find out if they’re destructive.

3. Content creation

Another increasingly common use case for language models is content creation. LLMS allows users to create a range of written content, from blogs and articles to short stories, summaries, scripts, questionnaires, surveys and social media posts. The quality of these results depends on the information provided in the original call.

When LLMs are not used directly to generate content, they can also be used to generate ideas. According to Hubspot use 33% of marketers use AI to generate ideas or inspiration for marketing content. The main value here is that AI can speed up the content creation process.

It’s worth noting that there are also tools like DALL-E, MidJourney and Stable diffusion exist that allow users to generate images based on a written request.

4. Search tool

Many users will have initially experimented with generative AI as an alternative search tool. Users can ask a chatbot questions in natural language and receive an immediate response with insights and facts on potentially any topic.

While using solutions like Bard or ChatGPT as a search tool provides access to a wide range of information, it is important to be aware that not all of this content is accurate.

Language models are prone to hallucinations and tend to make up facts and figures. For this reason, it is a good idea for users to verify all the information presented by the language models to avoid being misled by misinformation.

5. Virtual assistants and customer support

Generative AI also seems promising in the area of ​​customer support in the form of virtual assistants.

McKinsey has found that… a company with 5,000 customer service representatives Using generative AI increased problem solving by 14% per hour and reduced processing time by 9%.

AI virtual assistants enable customers to ask questions about services and products, request refunds and report complaints instantly. End users eliminate the need to wait for a human support representative and employees automate repetitive support tasks.

6. Detection and prevention of cyberattacks

Another interesting use case for language models in cybersecurity is cyberattack detection. This is because LLMs are capable of processing large data sets collected across a corporate network and can detect patterns that indicate a cyberattack and generate an alert.

To date, a number of cybersecurity solution providers have begun experimenting with this threat detection technology. For example, SentinelOne had one earlier this year LLM supported solution launched that can automatically scan for threats and initiate automated responses to malicious activity.

Another approach, that of Microsoft Security Copilot demonstrated allows users to scan their environments for known vulnerabilities and exploits and generate reports on potential security events in minutes so human defenders can respond.

7. Code development

Generative AI tools are not only capable of generating natural language, but also code in languages ​​such as JavaScript, Python, PHP, Java and C#.

The code generation capabilities of language models allow even non-technical users to generate simple code. Although they can write code for simple projects that solve basic problems, they have difficulty tackling more complex tasks of larger scope and scale.

Therefore, programmers should review code for functionality and security issues during development to avoid disruptions after deployment.

They can also help troubleshoot existing code or even create accompanying documentation so that users don’t have to spend time on manual work.

8. Transcription

LLMs are also gaining a lot of attention due to their ability to transcribe audio or video files into written text with high accuracy. Providers like Sonix use generative AI to create and summarize transcripts from audio and video files.

This means that human users do not have to spend time manually transcribing audio files, which can save significant time and eliminate the need to invest in a transcriptionist.

One of the advantages of LLMs over traditional transcription software is that, thanks to natural language processing (NLP), these tools are able to infer the context and meaning of the statements delivered via audio.

9. Market research

Generative AI’s ability to summarize and draw conclusions from large data sets makes it a useful tool for conducting market research to gain insights about products, services, markets, competitors and customers.

Language models can process a user’s text input or a data set and produce a written summary of trends and provide insights into buyer personas, competitive differentiation, market gaps, and other information that you can use to grow your business long-term.

10. Keyword research

AI assistants can also play a valuable role in streamlining keyword research. For example, users can ask for the best keywords for a potential topic, along with relevant sub-terms.

For example, you might ask for a list of SEO-friendly titles for a website’s blog. For best results, it’s best to use LLMs like ChatGPT to identify potential keywords and then use a third-party tool like Ahrefs or Wordstream to ensure there is significant traffic.

11. Sales automation

Generative AI tools like ChatGPT can also be used to automate specific segments of the sales process, from lead generation to nurturing, personalization, and qualification to lead scoring and forecasting.

For example, an LLM can analyze a data set and identify potential leads while developing an understanding of their preferences and creating personalized recommendations.

Likewise, when used for sales forecasting, the solution can process a data set, identify potential patterns, and estimate future sales and the amount of revenue that will arrive.

12. Sentiment Analysis

LLMs can be used as a qualitative analysis tool to analyze the mood of a text and determine the author’s attitude towards a particular topic.

This allows a company to evaluate customer opinions from sources such as social media comments and customer reviews to gain insights that allow them to better manage their brands.

For example, sentiment analysis can highlight keywords that customers use to describe a brand or product and reveal which features or capabilities they believe are most important for products, which can help guide future marketing efforts.

Language models: Making useless data usable

In any situation where you want to summarize a data set or gain insights from it, language models play a role. As LLMs evolve and more companies experiment with potential use cases, organizations will have a better understanding of risk and mitigation of some downsides such as factual hallucination.