Artificial intelligence is becoming an increasingly integral part of our everyday lives, both in business and in our private lives. We are increasingly turning to ChatGPT for quick answers to questions, using Google's AI mode for in-depth web searches, and conveniently controlling our smartphone apps via Siri. In a business context in particular, Slack AI helps us summarize important conversations compactly, Adobe Firefly gives our creativity a huge boost, and generative development systems such as Replit, Loveable, or Bolt offer us the chance to create complete software with nothing more than a few everyday language instructions.
All these and many other AI tools designed to make our lives easier are essentially based on so-called large language models (LLMs): you make an input in natural language, which the respective engine analyzes, interprets, and ultimately translates into a suitable solution.
But what exactly are LLMs, how do they work, and in which areas can companies use them to their advantage? Find out in this article!
What are large language models?
Large language models are AI systems that can process, interpret, and generate human language. They are based on deep neural networks that learn from an enormous amount of digital text. Through this training, they recognize statistical patterns in language and can ultimately formulate new content based on these patterns in a way that is understandable, logical, and natural. In this way, they capture context, meaning, style, grammar, and even specific areas of knowledge based on human commands, questions, statements, etc.
As soon as you make an input in an LLM tool, the model analyzes the wording and generates a response that matches the pattern of your request. This mechanism can be used for much more than just word processing. So-called multimodal LLMs (MLLMs) use special encoders that translate image, video, or audio data into a common numerical representation, enabling them to understand corresponding content in a similar way to text and create new content from it on demand.
Despite all their capabilities, large language models do not usually stand alone. Although they form a central element in today's AI structure, as they standardize language as a natural interface, they are usually part of larger systems. Only when combined with complementary modules do they enable everyday functions such as AI text editing, automatic video editing suggestions, or context-related sound analysis.
Companies benefit particularly strongly from LLMs because they can convert unstructured information into usable knowledge, drive creative work, and provide key impetus for AI automation. They save time, reduce the risk of manual errors, and create intuitive access to AI-supported applications. For many people, they are the most common point of contact with artificial intelligence.
How do LLMs work?
Large language models are based on methods from machine learning, deep learning, and various forms of neural networks. The learning process starts with a gigantic amount of text that the model analyzes. This enables it to recognize patterns, dependencies, and typical structures of language. These patterns function like probability chains: the model calculates the most plausible option for each possible next word and thus constructs a response – at lightning speed.
A good example is autocomplete in emails. Here, LLM-supported software can mimic your writing style and suggest phrases that statistically match your sentences. In another use case, automatic summaries are provided in tools such as Teams, Slack, or even in online shopping on Amazon. Here, the model recognizes the entire interaction, identifies important points, and uses them to formulate a short, understandable overview.
As we have already indicated, however, additional systems are needed to enable us to truly utilize LLMs in everyday (business) life: web interfaces, chat interfaces, API interfaces, and integrated business applications that integrate the respective model into specific processes. This results in extensive functions for customer service automation, automated accounting, code generators, and much more. The LLMs themselves only analyze and generate text or speech; it is the surrounding technologies that determine how this data is executed or further processed.
What can large language models do for businesses?
Large language models give companies very easy access to AI at a very basic level – and thus to automated analysis, creative assistance, or simply quick answers to complex questions. Teams can work much more productively because many tasks can be supported or even completely solved using speech or short inputs.
LLM-driven systems that link complementary modules are capable of structuring processes, retrieving data, or further processing content, making them powerful tools that can be used to massively optimize entire workflows.
These features create a versatile tool that can be flexibly integrated into almost any digital environment – and is increasingly becoming a central foundation for modern business processes.
Word processing – write and analyze more efficiently and structure content faster
Many organizations use large language models to create, improve, or structure texts more easily. The models recognize connections quickly and with high certainty, sort content reliably, and ultimately generate clear formulations that you can reuse directly. This makes it possible to edit reports, notes, or emails much more efficiently.
This also offers major advantages for developers and digital startups: automatic code generation has always been seen as one of the most promising areas of application for LLMs. Such systems facilitate the implementation of complex functions and the launch of new projects. The ability to classify information clearly is also particularly helpful. It allows teams to sort or prioritize content without having to check every detail manually.
Customer service automation –better understand inquiries, speed up responses, and reduce the workload on support teams
Automated customer service is already the rule rather than the exception in many areas today. LLMs analyze incoming messages, recognize intent and tone, and formulate appropriate responses without the intervention of an employee. Corresponding chatbots can resolve inquiries faster and around the clock, but also forward (potential) buyers to the right places for personal contact. At the same time, the system learns from conversations and continuously improves its assessments.
In practice, this means that prospects and customers receive immediately understandable information, and service teams are greatly relieved. Large language models recognize recurring patterns and suggest solutions based on previously collected cases. Some tools combine LLMs with knowledge databases to ensure that answers are technically accurate. Such functions can be seamlessly integrated into support portals, live chats, or help desk systems.
Medicine and science –recognizing complex patterns, accelerating research, and supporting diagnostic processes
Large language models are also playing an increasingly important role in research and healthcare. They can analyze complex structures of proteins, molecules, or genetic sequences and reveal patterns that would be difficult for humans to recognize. This ability helps in the development of new therapies, the screening of potential drugs, and the evaluation of medical literature. In addition, the models support medical professionals by (once again) structuring information or filtering out relevant information from long documents.
Some systems use LLMs to prepare simple assessments in the early stages of examination. They classify symptoms, structure conversation data, or support digital recording processes. Such applications do not replace medical expertise, but they do facilitate preparation and documentation. This shortens the time between initial analysis and informed assessment.
Researchers use LLMs to summarize data or derive hypotheses from existing findings. This accelerates scientific work and provides valuable impetus for new studies.
Marketing, banking, law –improve decisions, identify risks early, and solve text-based tasks faster
In marketing, LLMs help analyze trends, assess sentiment, and develop new ideas. The models (often integrated into larger suites) provide target group-focused suggestions for texts, campaigns, or short advertising messages. Companies benefit from more effective customer acquisition processes thanks to the clear focus of their content. In addition, automated text analysis facilitates search engine optimization (SEO). LLMs connected to the internet can even reliably identify important search terms (keywords) and topics with a direct view of the strongest competitors.
Financial institutions primarily use large language models to check transaction data and detect anomalies. They provide clues to possible cases of fraud. This automated preparatory work enables teams to identify risks more quickly and take targeted action.
In legal work, LLMs are used to analyze documents or structure complex issues. For example, the models can provide reliable tips in near real time on whether a ruling is likely or unlikely by analyzing the facts in the law objectively and at lightning speed.
Conclusion
LLMs are at the heart of many modern AI applications. They play a key role in making artificial intelligence accessible to everyone – simply by using natural language. By putting words or phrases into a form that machines can understand and process, they create an intuitive interface.
This offers significant advantages, especially for companies. In everyday business, LLM-driven systems can independently interpret information, recognize important connections, and clearly structure content. They provide new ideas, improve communication, and support teams in the secure evaluation of large amounts of data.
This creates an environment in which you can work more effectively simply by using natural language, and many processes require less time. This is precisely what makes LLMs a central component of AI development, one that is having a lasting impact on our working world and our everyday lives.
FAQ
Is AI always a large language model (LLM)?
No. Many types of AI work completely differently from an LLM. Some models analyze images exclusively, while others specialize in numbers or sensor data. Nevertheless, you encounter LLMs particularly often in everyday life because they process language and are therefore very accessible.
What is the difference between LLM and NLP?
NLP (Natural Language Processing) describes the entire field that deals with the processing of natural language. An LLM is a special technological model within this field that can solve language-based tasks. Based on machine learning, deep learning, and neural networks, LLMs learn from enormous amounts of text, which in turn enables them to analyze content, answer questions in conversational style, or create texts. NLP provides the framework for this, while LLMs deliver the specific capabilities.
What does LLM mean?
"LLM" is an acronym for "Large Language Model." The term describes an AI model that can understand, interpret, and generate human language. Such systems are based on deep neural networks and are trained with enormous amounts of text data. The result is a technology that allows you to interact in natural language. LLM tools such as ChatGPT are able to analyze content and formulate responses that seem natural and understandable to you.
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Jens ist Mitgründer von bakedwith, einer Boutique-Beratung für smarte Automatisierung und KI. Er unterstützt mittelständische Unternehmen und Konzerne dabei, Prozesse zu optimieren, manuelle Arbeit zu reduzieren und durch effiziente Workflows Wachstum zu erzielen. Zuvor war Jens Growth Lead bei OMR und begleitete als Freelancer zahlreiche Unternehmen bei der Optimierung ihrer CRM- und Automationssysteme. Seine Leidenschaft gilt der Verbindung von Wachstum und Effizienz – damit Teams sich auf das Wesentliche konzentrieren können.
