Machine learning (ML) and large language models (LLM) solve different tasks in AI. This article explains in an understandable way what the difference is and when which model is suitable.
Not all artificial intelligence (AI) is the same. Anyone who looks into the subject will quickly come across terms such as machine learning (ML) and large language models (LLM). But what exactly is behind these technologies, and which is suitable for which application?
Machine learning is a subfield of artificial intelligence in which an algorithm learns from existing data to recognize patterns and use them to predict future events or derive decisions. To do this, the model is fed with training data and learns through optimization which features are relevant for the result.
A large language model is a specialized AI language model that has been trained on billions of texts to understand, interpret, and generate new content. LLMs are based on so-called transformer architectures, which are capable of capturing context across large areas of text.
Overview of differences between ML and LLM
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AI Model
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Maschine Learning (ML)
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Large Language Model (LLM)
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Focus
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Data patterns, predictions
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Language comprehension and text generation
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Data type
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Structured data
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Unstructured text data
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Traninig basis
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Statistical methods with large amounts of data
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Millions to billions of text data
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Typical use
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Quality forecasts, recommendations, clustering
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Chatbots, text creation, automatic analyses
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What is the difference between AI and Machine Learning?
AI is the broad concept of systems that mimic human intelligence. Machine Learning is a subset of AI where algorithms learn from data instead of following fixed rules.
When is AI worth implementing?
AI creates value when structured, high-quality data is available and the benefit is measurable — for example, through time savings, fewer errors, or improved forecasting. Without data readiness, AI adds no real value.
How do I start an AI project?
Start with a data and process assessment, followed by a proof of concept (PoC) to validate feasibility and ROI. If the results are positive, move to pilot and production phases.
What are the prerequisites for using AI?
You need clean, structured data, documented processes, available interfaces, computing infrastructure (cloud or on-prem), and internal ownership. These factors determine how well AI can perform.
What types of AI exist?
The main types include rule-based systems, machine learning, deep learning, natural language processing (NLP), and computer vision. Each serves different use cases and data requirements.
Does AI replace human work?
No. AI handles repetitive, data-driven tasks, while humans interpret, decide, and guide strategy. The strongest results come from combining human expertise with machine precision.
What kind of data does AI need?
AI needs high-quality, structured data, e,g. from production systems, sensors, text, or images. Data consistency and accuracy directly determine the reliability of AI outcomes.
How is the success of an AI project measured?
Success is measured through key metrics such as error reduction, time savings, production efficiency, or ROI. An AI project is successful when it delivers a clear operational or business advantage.
We support you in making the right decisions.
Machine learning is the better choice when structured data is available, such as numbers, tables, or measured values. When it comes to making accurate predictions, such as detecting anomalies or planning demand and resources, ML is often more efficient. ML also offers advantages when IT resources are limited, as the computing effort is usually lower.
LLMs are particularly useful when unstructured text needs to be analyzed or generated. They are suitable for understanding language, summarizing content, or writing new text. For applications with direct user interaction, such as chatbots or intelligent assistance systems, LLMs are usually the better choice.
Sofia Steninger
Solution Sales Manager
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