AI Cologne

  TL; DR "'Kölle'—what's that? Our users are asking for 'the most beautiful city in the world,' and the chatbot has no idea what they mean? Multi-Query Retrieval can help. Multi-Query Retrieval is an advanced concept within Retrieval-Augmented Generation (RAG) architectures. It aims to improve the quality and diversity of the retrieved contextual information from a knowledge base before it's passed to a Large Language Model (LLM) to answer a query. Introduction The RAG (Retrieval-Augmented Generation) approach provides a solid foundation for delivering meaningful and relevant content for user queries. It's used in Knowledge Management and, for example, in creating situational, personalized assistants. Over time, however, shortcomings in standard RAG have been identified, and corresponding solutions proposed. These improvements target one of the three RAG areas: Retrieve, Augment, or Generate . In today's blog post, we will look at a specific techniq...

TLDR; Evaluation of AI systems is essential for safety and satisfaction of our users. Evaluation of stochastic AI systems is a lot more complex than your typical deterministic enterprise application. Not only does this require an understanding of user needs, non-functional system requirement, model capabilities but also their intersections to pick the right evaluation approach. A sound approach rests on a clear blue print, the right evaluation metrics, and automatization for transparency. A structured evaluation approach is a must. Industry grade LLM-application development frameworks facilitate implementation. Introduction AI assistants like chatbots excel in use cases where know-how needs to be mediated. Whether this is a cooking recipe, the explanation of a complex physical topic or a piece of interesting trivia. In our current project we apply LLMs to disseminate travel information to people interested in the beautiful city of Cologne. You want to know something about thi...