Crafting the Future: AI Platforms and Chatbot Reliability Essentials | hybrid

Cloud Native Night Munich

Join us for an engaging meetup featuring two expert talks on the forefront of AI technology. Gain valuable insights into how AI platforms can turbocharge innovation by streamlining company-wide AI development and deployment. Additionally, explore the latest advancements in chatbot testing techniques, with a focus on ensuring the reliability and accuracy of chatbot responses through innovative methods. If you want to deepen your understanding of cutting-edge AI solutions and get to know best practices, join us and have insightful discussions with our community!

THIS MEETUP WILL BE HYBRID. | Zoom

*** AGENDA ***

• 6.30 PM | Doors open. Grab some snacks & drinks.
• 7.00 PM | Welcome & Intro – Start of hybrid meetup
• 7.15 PM | Don't Forget to Test: Crafting Reliable Chatbots, Sebastian Macke (Lead Software Engineer, QAware)
• ~8.15 PM | Turbocharging AI Innovation: How AI Platforms Enable The Bulletproof Deployment of GenAI Use Cases, Mario-Leander Reimer (CTO, QAware)
• afterwards: More snacks & drinks, and a lot of time for networking with all attendees and speakers.

*** ABSTRACTS ***

Don't Forget to Test: Crafting Reliable Chatbots,
Sebastian Macke (Lead Software Engineer, QAware)

Traditional approaches to testing don't work with chatbots and RAG (Chat with your data) applications! Simply comparing a response to an optimal answer is impossible due to the statistical nature of LLMs and their tendency for hallucinations. Additionally tests are difficult because of the subjectivity and scope. What makes up for a good answer and how does a chatbot react if you ask questions outside of the defined rules? We need to think about new testing techniques and resulting KPIs (Key Performance Indicators) to ensure the reliability, accuracy, faithfulness and relevance of our chatbots responses. These techniques are still being researched and may need to be adapted to fit specific use cases. We will outline the current state of research regarding chatbot-testing, present a set of KPIs important for chatbots, demonstrate the state of the test frameworks and finish with an outlook about future developments.

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Turbocharging AI Innovation: How AI Platforms Enable The Bulletproof Deployment of GenAI Use Cases,
Mario-Leander Reimer (CTO, QAware)

Generative AI is the talk of the town. Anyone who spends just five minutes thinking about AI can surely come up with several useful business use cases. However, all too often, we find ourselves facing the following dilemma: we want to quickly launch our chatbots and assistant systems and bring our ideas to market readiness. Yet at the same time, important, complex, cross-functional aspects such as data protection, compliance, operational readiness, or model fine-tuning often slow down rapid development and deployment. Furthermore, enterprise scale AI projects often involve many different stakeholders: data engineers, AI specialists, software engineers, operational experts, and business departments. Too much talking and no progress at all are the result. AI platforms to the rescue! We believe that established platform engineering approaches and technologies, combined with LLM Ops practices, can tackle this dilemma. Only a robust, scalable, and flexible platform enables our teams to efficiently develop, operate, and manage their data, models, and applications. The platform hides the inherent technical complexity, while allowing users to fully focus on the use case and the creation of value and innovation. We will explore what a corporate AI platform can look like and the components and services it requires. We discuss how a company-wide platform strategy not only simplifies technical implementation but also creates an ecosystem for innovation, fosters collaboration, increases reusability, and ultimately drastically shortens the time to market.

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