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How external data makes AI more reliable - Retrieval augmented generation explained
Artificial intelligence (AI) is changing the world of work - but many companies are faced with the challenge of incomplete or unreliable answers from language models. Retrieval Augmented Generation (RAG) offers a solution: the method combines AI systems with external, verifiable data sources to create comprehensible, up-to-date and fact-based results.
Making complex data easy to understand
Data often contains more knowledge than is apparent at first glance. Small and medium-sized enterprises (SMEs) in particular face the challenge of making meaningful use of unstructured information such as customer feedback or operating logs. A new method from research can help with this: so-called hyperbag graphs. They represent data as networks in which connections, patterns and weightings between different elements can be identified – for example, how often certain topics occur together.