A large language model (LLM) is an AI model trained on very large amounts of text that can process and generate natural language on this basis. LLMs are the technical foundation of AI assistants such as ChatGPT, Claude and Gemini.
An LLM is a computer program that has learned how language works from huge amounts of text. It can answer questions, write, translate and summarise texts – comparable to a very well-read assistant who can, however, also make convincing-sounding mistakes.
Why do I need to know this?
LLMs are now part of many tools that shop and website operators touch every day: AI assistants that make purchase recommendations, customer service chatbots, text generation tools and search functions. Understanding the basic principles helps you judge what the technology is suitable for, where its limits lie and how it affects your own visibility – see Generative Engine Optimization.
Technically, current LLMs are generally based on the transformer architecture. During training, the model learns statistical patterns from text; at runtime it predicts the most likely continuation word by word. Two important properties follow from this: a model's knowledge ends at its training cut-off date, and answers can sound plausible while being factually wrong – so-called hallucinations. Techniques such as RAG mitigate both weaknesses by supplying the model with up-to-date external data.
Practical relevance for shop and website operators
In e-commerce, LLMs are typically used for product descriptions, translations, category texts, product data enrichment, customer service chatbots and internal automation. Properly integrated, they can speed up repetitive text work considerably and improve data quality. Our pages on AI solutions and AI automation and the article AI automation in e-commerce show which scenarios may suit your business.
In practice, the question of the operating model arises when choosing a solution: cloud services from major providers are quickly ready for use and continuously improved, but require careful review of data flows and contract terms. Open models, by contrast, can be run on your own infrastructure, which offers more control over data but requires expertise and operational effort. Which option fits depends on your data situation, budget and use case.
Typical mistakes
- Publishing AI-generated texts unchecked – hallucinations can lead to incorrect product information and legal risks
- Entering confidential customer or company data into public AI tools without a privacy review
- Mass-producing generic AI texts that barely differ from competitors and add no value
- Overestimating freshness: without a connection to external data sources, an LLM knows neither your stock levels nor current prices
- Underestimating costs and operations – API usage, quality assurance and maintenance of the integration belong in the calculation
What to look out for
A clearly scoped use case with human review has generally proven effective: the LLM produces drafts, people check and approve them. Also pay attention to the provider's data protection compliance (data processing agreement, server location, no use of your data for training), transparent costs per operation, and the ability to feed results back into your existing systems such as your shop or PIM.
LLMs specialise in language. Other AI methods – for image recognition, forecasting or price optimisation, for example – work on different principles. Which technology fits depends on the specific use case.