LLM integration: systematic engineering and evaluation
The rapid adoption of large language models (LLMs) in the corporate landscape is fundamentally transforming software-intensive systems. While integrating these AI models promises enormous gains in efficiency, it also increases the complexity of quality assurance and system stability. Since LLMs often operate probabilistically—meaning they are not always precisely predictable—companies face a new challenge: How can the reliability of an AI solution be guaranteed? Relying solely on “trial and error” here risks poor decisions and security vulnerabilities.
The key to success lies in systematic AI engineering. Only those capable of objectively evaluating the performance of language models can develop AI applications that will stand the test of time in production environments. Learn how to make the leap from mere experimentation to a robust, professional software architecture with LLMs.
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- In-depth evaluation: using practical examples and concrete strategies, you will learn how to systematically evaluate LLMs and LLM-powered systems to leverage AI effectively within your organization.
- Reliable quality assurance: you will learn methods for objectively assessing the quality and reliability of LLM outputs and minimizing risks (such as hallucinations).
- Strategic selection: you will learn the process for selecting the technologically and economically appropriate model for your specific requirements.
- Future-proof integration: you will understand the impact of LLM integration on the entire software lifecycle and prepare your IT infrastructure for the next stage of digitalization.
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This event is aimed at technical professionals and decision-makers in the fields of software development and AI systems.
- Software developers and systems engineers
- AI engineers and machine learning engineers
- IT architects and software architects
- DevOps and platform engineers
- Technical project managers in AI and software projects
- Professionals from industries such as energy, manufacturing, automotive, and defense
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- Format: webinar / lecture
- Duration: flexible
- Delivery: online or in-person
- Practical component: high, with examples and specific application scenarios
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The format can be customized to suit your organization’s specific needs—for example, by:
- Incorporating your specific use cases
- Adapting to your industry or project type
- Delving deeper into specific methods or tools
