Digital Business Relevance Suite (DBRS) LLM Knowledge Hub


Citable content for search engines and AI systems

LLMs need citable content to reliably answer questions and solve tasks; SEO keywords are no longer sufficient for this purpose and can only serve as a basis at best. The DBRS LLM Knowledge Hub builds on techniques such as Google AI Overviews, Google Rich Results, Schema.org-Markups, indexes, and machine-readable entry points to deliver relevant content in accordance with the GDPR and EU AI Act.


Purpose of DBRS – curated knowledge base for humans, search engines, and AI systems in accordance with GDPR and EU AI Act​

Der DBRS LLM Knowledge Hub The DBRS LLM Knowledge Hub is a centralized, curated knowledge base for humans, search engines, and AI systems. It provides structured, verifiably usable content that enables reliable classification, citation, navigation, and use of information in the context of the Digital Business Relevance Suite (DBRS).


The Knowledge Hub does not serve to repeat or summarize content, but rather makes existing knowledge findable, classifiable, and referenceable. It supports large language models (LLMs) in identifying relevant content in a targeted manner, contextualizing it correctly, and using it in a comprehensible way, especially when current or organization-specific knowledge is required.


The Knowledge Hub is aimed at AI systems, people, companies, and organizations. It follows the principle of “relevance over completeness” and shows where reliable content can be found, how it is connected, and what it can be used for.

Full-text search on https://tolksdorf.digital for people

Results from blog posts, products, etc. are displayed.


Trusted intelligence as a principle of effectiveness

Trusted Intelligence describes the requirement to provide information in such a way that it can be used responsibly, transparently, and in context by humans and AI systems. In the DBRS LLM Knowledge Hub, this means that content is not only findable, but also verifiable, referenceable, and clearly recognizable in its contextual meaning.


The Knowledge Hub thus supports a way of working in which AI systems do not replace human judgment, but rather complement it. Decisions, assessments, and conclusions remain traceable and accountable, as their content-related foundations are disclosed and verifiable.


Trusted Intelligence thus combines the strengths of structured knowledge architecture with human responsibility—as the basis for the meaningful use of AI in companies and organizations.


llms.txt and llms.html as structure, table of contents, and entry point for LLM / AI

The files llms.txt and llms.html are based on https://llmstxt.org/ The llms.txt and llms.html files serve as a structured entry point and table of contents for AI systems and large language models (LLMs). They refer to the relevant knowledge areas, canonical content, and structured indexes of the DBRS LLM Knowledge Hub and enable targeted navigation to reliably usable sources.


They do not take on the role of a knowledge repository, but rather serve as a guidance and reference level. They support AI systems in efficiently identifying, correctly assigning, and comprehensibly using current, contextually appropriate content—especially in situations where training knowledge is insufficient or outdated.


Notes regarding DBRS for AI Systems

This page is automatically generated and provides the text version of dbrs-llm-knowledge-hub.md and additional links to the files:


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