Isn't that what ChatGPT is for?<\/h3>\n\n\n\n
AI systems such as ChatGPT or Gemini are based on publicly available data and impress with their ability to provide a wide range of knowledge quickly and easily. However, when it comes to specific, internal business issues, these systems naturally reach their limits. They are unfamiliar with a company\u2019s individual processes or specific challenges, as this data is generally not included in their training datasets. In a specific business context, such systems therefore offer only limited value, since users must research and provide the necessary information themselves. Even if AI models have been trained with specific information, internal company processes and data are subject to constant change. Retraining such models is technically challenging, resource-intensive, and often not even possible for public AI models.<\/p>\n\n\n\n
In addition, we must ask ourselves what information should even be shared with a public provider of AI systems. If the data is not to leave the company, then the LLM would have to be operated in-house (e.g., by using an open-source model).<\/p>\n\n\n\n
AI that can seamlessly access company-specific knowledge sources has the potential to fundamentally transform how information is handled within an organization. It provides precise and context-sensitive answers tailored to the company\u2019s specific needs. Employees benefit from easy access to knowledge by being able to ask for it using natural language. This speeds up processes, boosts productivity, and simultaneously increases the value of knowledge management. Only through the efficient use of internal data sources does the sustainable maintenance of the knowledge base become viable. These advantages make AI with domain knowledge an essential tool for the future.<\/p>\n\n\n\n
Retrieval-Augmented Generation (RAG)<\/h3>\n\n\n\n
Retrieval-Augmented Generation (RAG) is a method that combines the rapid retrieval of relevant information (information retrieval) from data sources with text generation by generative AI. The retrieved information is used to generate specific answers. Instead of merely presenting search results from knowledge sources or relying exclusively on the training data of AI models, RAG combines the advantages of both approaches to deliver context-aware responses.<\/p>\n\n\n\n Based on a query, relevant information is retrieved from internal data sources. To do this, the information is first transferred from the source systems to a vector database. This process involves two key steps: First, the information is broken down into smaller chunks (chunking); then, these chunks are converted into numerical vectors (embedding). These numerical representations make it possible to efficiently find information that semantically matches the query.<\/p>\n\n\n\n The information found is then forwarded, along with the original query, to the system\u2019s \u201ebrain\u201c\u2014the Large Language Model (LLM). Unlike the information from training data, which is stored structurally within the AI model, the data from the knowledge base is processed dynamically as part of the user\u2019s query. In the final step, the AI can generate the response based on this information.<\/p>\n\n\n\n The benefits of RAG are immediately apparent:<\/strong><\/p>\n\n\n\n \u2022 Precision:<\/strong> Answers are based directly on domain-specific sources and are therefore highly relevant.<\/p>\n\n\n\n \u2022 Timeliness:<\/strong> Information from existing data sources can be used immediately without requiring any adjustments to the AI model.<\/p>\n\n\n\n \u2022 Flexibility:<\/strong> New data sources can be seamlessly integrated, allowing for a dynamic response to changing requirements and knowledge bases.<\/p>\n\n\n\n To take advantage of RAG at IT Sonix, an internal chatbot was developed. This chatbot draws on information from the internal Confluence system to generate context-aware responses. The data is automatically retrieved via the Confluence API, processed using techniques such as chunking and embedding, and stored in a vector database. Metadata, such as links to the original Confluence page, is added to ensure the source is traceable. This makes it possible to find semantically relevant information quickly and accurately. When a query is submitted, the vector database is searched for relevant information to serve as the basis for the response. This response is then generated using open-source AI hosted by IT Sonix itself, ensuring that internal information does not leave the company. In addition to the generated response, the chatbot provides links to the relevant Confluence pages so that users can view the sources directly. In this way, the IT Sonix chatbot makes corporate knowledge specifically available, ensures traceability, and at the same time does not share any data with external systems.<\/p>\n\n\n\n Although RAG is a promising technology, there are challenges that must be addressed in order to realize its full potential:<\/p>\n\n\n\n Sensitivity:<\/strong> Finding the right information can depend heavily on the exact wording of the user's query. This can result in the relevant context not being found. Improvements such as re-ranking algorithms, context-based enhancements to individual chunks, or additional tagging can help solve this problem.<\/p>\n\n\n\n Safety considerations:<\/strong> Safety mechanisms for AI outputs (guardrails) and smart rights management must be established to ensure responses that are appropriate for the company. Users should only be able to access information that has been approved for them. This can be implemented by integrating Active Directory (AD) and including the corresponding metadata for access rights in the knowledge base. At the same time, processes must be in place to enable rapid updates, such as when user access is blocked or modified.<\/p>\n\n\n\n Continuous Improvement:<\/strong> The rapid advancements in generative AI and RAG offer numerous new possibilities, but also require ongoing evaluation. Approaches such as agent-based RAG, which integrates reflection, planning, and the use of additional tools such as internet searches or the creation of visualizations and graphics, significantly expand functionality but must be understood and used appropriately.<\/p>\n\n\n\n Despite these challenges, it is clear that RAG is a forward-looking technology that, with the right optimizations and security measures, offers tremendous value to businesses.<\/p>","protected":false},"excerpt":{"rendered":" Wie k\u00f6nnen Unternehmen ihr Wissen so zug\u00e4nglich machen, dass man effizient darauf zugreifen und es unkompliziert nutzen kann? Diese Frage ist f\u00fcr Unternehmen von zeitloser Bedeutung, insbesondere dann, wenn mit wachsender Gr\u00f6\u00dfe auch die Komplexit\u00e4t und Vielfalt des Firmenwissens zunimmt. K\u00fcnstliche Intelligenz (KI) hat sich seit dem Aufkommen von ChatGPT als leistungsstarkes Werkzeug etabliert, um […]<\/p>\n","protected":false},"author":4,"featured_media":621,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[6],"tags":[],"class_list":["post-620","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-kuenstliche-intelligenz"],"acf":[],"yoast_head":"\n![\"\"](\"https:\/\/test.itsonix.eu\/wp-content\/uploads\/2026\/05\/RAG-AI-ChatBot_DE-1024x622.jpg\" "\\"RAG-AI-ChatBot_DE\\"")
<\/figure>\n\n\n\nIT Sonix Chatbot<\/h3>\n\n\n\n
Example: IT Sonix Chatbot:<\/p>\n\n\n\n![\"\"](\"https:\/\/test.itsonix.eu\/wp-content\/uploads\/2026\/05\/grafik-ki-rag-1.png\")
<\/figure>\n\n\n\n![\"\"](\"https:\/\/test.itsonix.eu\/wp-content\/uploads\/2026\/05\/grafik-ki-2.png\")
<\/figure>\n\n\n\nChallenges and Outlook<\/h3>\n\n\n\n