What is Generative AI?

Generative AI is a type of artificial intelligence (AI) that aims to create something new, such as texts, images, music or videos. Unlike other types of AI, which are designed to analyze existing information or perform specific tasks, generative AI attempts to create something new based on what it has learned from a large amount of data. It uses patterns and structures in the data to generate new content that is similar to what it has seen before. It is, so to speak, a creative machine that is able to think and create in a way that was previously only possible for humans. Generative AI is used in many areas, from art and entertainment to research and development.

What is a Large Language Model (LLM)?

An LLM (Large Language Model) is a computer program that has been specially developed to understand and generate texts. It can answer questions, hold conversations, translate texts and much more. LLMs are trained with large amounts of text so that they learn how to react appropriately in different situations. A well-known example of such a model is ChatGPT from OpenAI. But Google Gemini or SwissGPT from AlpineAI are also LLMs.

What are Generative AI or LLM-Usecases?

The term generative AI or LLM use cases refers to applications that can be implemented with an LLM. LLMs have the great advantage that they can supplement or scale our human work. Of course, LLMs cannot support just any human ability or work. There are successful, meaningful and target-oriented LLM applications and use cases for LLMs.

What are successful LLM use cases?

The use of Large Language Models (LLMs) should create added value for users and provide them with optimum support in their daily work. However, this requires users to know and be able to identify the relevant use cases and possible applications of an LLM. Unnecessary use of LLMs where the model is not required only leads to a waste of resources. However, if an LLM is used incorrectly or for inappropriate applications, this can even lead to an increased workload for employees. It is essential to understand that not every company has the same use cases for LLMs. Instead, each company must individually determine which use cases are valuable for its organization.

Where can we find successful LLM use cases?

In my research, I looked at various tools, templates and methods that can be helpful in identifying suitable LLM use cases. The approaches of data drivers and the AI Foundation were particularly inspiring. On this basis, I have created the LLM Usecase Finder as a template that you can use to identify suitable applications for LLMs in your organization.

As a first step, you should record all data and fundamentals in your environment that are suitable for use in an LLM. Enter this information in the first column of my template. Once this has been done, think about which functions and processes could be supported by the LLM and write this in the fields in the second column. This results in various outputs that represent the result of using the LLM. These expenses belong in the third column.

Then go through the process again, but this time start with the desired expenses in the last column. Focus on your own wishes, those of your colleagues and, of course, the wishes and needs of your customers. Then switch to the middle column and enter the required functions and applications. Finally, add the appropriate input data in the first column.

By going through this process twice – once from the front and once from the back – you lay a solid foundation for the first LLM use cases or applications in your team or company.

How do I get the template for the LLM-Usecase Finder?

You can buy the template via the following button and then download it directly.

Who is the LLM-Usecase Finder suitable for?

The LLM-Usecase Finder is aimed at anyone who wants to optimize and expand their day-to-day work and business processes with Generative AI and LLMs.

The LLM-Usecase Finder is also worthwhile for all those who already work with technologies such as ChatGPT. The LLM Usecase Finder gives you completely new ideas.

What happens next?

Once you have found the most important applications for your LLM, it’s best to test them out straight away. You can use tools such as ChatGPT, SwissGPT, Gemini or Perplexity. Give the LLM the required data and formulate the task for the LLM.

But beware, formulating the task may present you with the next challenge. You will quickly realize that formulating a precise and accurate task is just as important as finding the right use cases. Take a look at my Prompt instructions included. Template for creating prompts to.

Further useful tips

Would you like further support with your AI project? Then hopefully these offers are just right for you.

Nothing there?

Then write me a message with your wishes and questions and we will find an offer for you. Just send me a message via WhatsApp or email.

Or come directly to my WhatsApp group – where I regularly post use cases, news, best practices, events and much more about chatbots, ChatGPT and co.