ON DEMAND WEBINAR: How intelligent are LLMs really?

The term Large Language Model (LLM) has become indispensable in any AI discussion – but what actually lies behind it? And what does it mean in concrete terms for a company planning an AI initiative?

This first webinar lays the foundation. It provides an honest, technically sound understanding of how modern language models work – and helps answer the questions that arise in every boardroom and strategy meeting:

• Should we invest in an AI initiative at all?
• When does an LLM project make sense?

• One key takeaway: LLMs are only one component of an AI initiative – not the solution in themselves. The quality of the underlying data is critical. A language model that is not supplied with the right, structured, and up-to-date data will fail – regardless of how powerful the model itself is. Garbage in, garbage out still applies in the age of generative AI and LLMs.

   

Your value in 30 minutes

• Principle: Next-token prediction, training vs. prompt engineering
• Strengths: Processing unstructured data, text and code generation
• Limitations: Lack of logical consistency and causes of hallucinations
• Success factors: Model selection, data quality, context engineering, evaluation, and safeguarding

Your Key Takeaways

A solid basis for decision-making to realistically assess the potential and limitations of LLMs – and to launch your first AI initiative on a sound foundation.

Our Speakers

Oliver Ruf Head of Consulting & Projects, Board Member & Partner

Prof. Dr. Andreas Martin  Visiting Applied AI Scientist

AI Webinar Series

In three 30-minute modules, Andreas Martin – Visiting Applied AI Scientist, PhD in Information Systems, and Professor of Applied AI at FHNW – provides a practical understanding of the most important AI developments of our time: from Large Language Models (LLMs) and Agentic AI to modern integration standards such as the Model Context Protocol (MCP).

• View the full AI webinar series
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• To Webinar #2: AI Agents
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• To Webinar #3: MCP and Skills