March 24, 2025 / Manuel Blümel

Large Language Models (LLMs) are advanced AI systems that are trained with extensive data sets to understand and generate human language. Their range of functions is impressively diverse - they analyze complex texts, create tailored content and even generate functioning code.

LLMs have already established themselves as valuable tools in e-commerce. Companies use them to efficiently create appealing product descriptions, optimize internal workflows and automate customer communication. 

However, these systems also have their own characteristic weaknesses. They tend to hallucinate, sometimes work with outdated information and reach their limits when it comes to domain expertise. Retrieval Augmented Generation (RAG) offers an effective solution to these problems.

Many companies are already relying on AI language models to increase their efficiency and offer automated services:

In addition to these successful examples, the automation of work processes in the after-sales area in particular can lead to considerable cost and time savings as well as an improved user experience. The relevance of handling these processes promptly, efficiently and qualitatively in the interests of the customer becomes particularly clear in Deloitte's Aftermarket Service Report: the service sector is the stabilizer for sales and profits in turbulent times.

The AI market is served by various providers - from versatile models such as OpenAI (ChatGPT) and Google (Gemini) to specialized solutions such as Google Translate API or Microsoft Copilot. Despite this variety of commercial options, companies face many implementation challenges, especially in business-critical application areas.

A key problem is so-called “hallucinations”, where the models generate incorrect or misleading information - a risk that can be unacceptable for business decisions or customer communication. The quality of the information generated does not always meet expectations, especially when it comes to specialized knowledge, confidential company data or current developments that are not included in the training data set.

Traditional approaches to optimization such as pre-training and fine-tuning allow for thematic specialization, but require large amounts of domain-specific data, significant computational resources and regular updates. In addition, there is a risk of catastrophic forgetting, whereby the potential of an LLM can be limited by fine-tuning. In the process, previously acquired basic knowledge and concepts are overwritten by newer information, resulting in a loss of overall performance or accuracy when working on tasks that require a broad understanding of various topics. This makes LLMs costly and time-consuming for many companies to implement.

In this context, Retrieval Augmented Generation (RAG) offers an optimal balance between performance and effort control. RAG combines the generative capabilities of LLMs with targeted information retrieval from trusted, proprietary sources, addressing the most critical weaknesses of traditional LLM implementations - without the need for costly retraining of the base models.

Retrieval Augmented Generation (RAG) is a modular technology that combines the capabilities of semantic search and external knowledge bases to act as an extension of existing LLM systems. RAG supports pre-trained LLMs by providing relevant context so that more accurate and factually correct answers can be generated. This is done by integrating additional data from external sources or knowledge databases via the RAG system. RAG is often realized with the help of semantic search in order to optimize the document search process. The semantic search provides the most relevant data, while RAG enriches the output of the language model with the latest information. This synergy can lead to unparalleled precision in AI-assisted answers. This allows relevant knowledge to be provided for specialized use cases that may not be contained in the LLM's training data set or that is more outdated than its current knowledge base.

When users make a request to the LLM, RAG ensures that relevant information is retrieved (Retrieve), the prompt is augmented with this information (Augmented) and finally the response is generated with the LLM (Generation).

RAG therefore improves the accuracy, timeliness and reliability of LLM responses, especially for domain-specific or rapidly changing information. The technology combines the generating power of LLMs with targeted information retrieval from the company's own sources. This opens up a wide range of possible applications:

• Context Based Chatbots
  Dialog systems that respond to user queries with precise, source-based answers and are capable of considering context across multiple interactions.

• Knowledge Management Systems
  Central platforms that provide access to internal company information from various sources (documentation, wikis, e-mails, presentations) and answer questions in natural language.

• Content Creation
  Assistance with text creation through automatic research, fact checking and source references, which increases the quality and credibility of the content.

• Customer Service Automation
  Systems that can answer customer queries in a personalized way by accessing product databases, customer history and support documentation.

In business-critical use, RAG offers decisive advantages over using LLMs without this approach. It ensures greater accuracy and reliability by significantly reducing hallucinations and factual errors, as verified sources are used instead of relying solely on the trained knowledge of the model. The timeliness of the information is guaranteed because the knowledge base can be continuously updated without the need for time-consuming retraining of the basic model. 

Transparency and traceability are increased by the ability to cite sources, which improves verifiability and promotes trust in AI systems - a particularly important aspect for business-critical applications. In addition, RAG enables personalization through tailored responses based on user-specific data, which offers real added value, especially in customer service and internal support requests.

RAG opens up new ways to tailor LLMs to individual needs and unleash their full potential. With RAG’s flexible range of application options, customized solutions can be developed that are perfectly tuned to the LLM use cases. The integration of an RAG system creates significant added value that makes the investment worthwhile.