Athanasios Papaioannou
AI Engineer, Agile Actors
Athanasios Papaioannou is an AI Engineer, currently working on LLM-driven applications and the wider NLP domain in a business context. While my background is in Electrical Engineering and Computer Science, I have also specialised heavily on AI-assisted digital signal processing applications both through my academic work during my PhD studies and through my previous working experience. My main interests include utilising traditional software and ML/DL concepts to enhance the performance of contemporary AI workflows.
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Upcoming Sessions
A.I. Summit
March, 13, 2025
13:15 -
13:45 EEST
30 MINS
The Evolution of Programming Languages with AI
Beyond the Hype: How Does RAGAS Measure Real LLM Impact?
In the rapidly evolving landscape of AI, separating genuine advancements from the noise is crucial for both industrial practitioners and academic researchers. The non-deterministic nature of LLM-driven applications amplifies this challenge, leading to difficulty in applying common software testing practices and thereby quantifying application performance.
This session aims to offer insight into mitigating these issues by delving into RAGAS (Real-World Application and Generalization Assessment Score), a novel framework designed to quantitatively measure the performance of LLM-driven applications. Drawing from well-established practices in the software testing world, it demonstrates how RAGAS can guide the development and deployment of LLM technologies with tangible benefits. A short demo is also included as a practical demonstration of how RAGAS's ability to evaluate LLM effectiveness can be translated into real-world impact.
This session aims to offer insight into mitigating these issues by delving into RAGAS (Real-World Application and Generalization Assessment Score), a novel framework designed to quantitatively measure the performance of LLM-driven applications. Drawing from well-established practices in the software testing world, it demonstrates how RAGAS can guide the development and deployment of LLM technologies with tangible benefits. A short demo is also included as a practical demonstration of how RAGAS's ability to evaluate LLM effectiveness can be translated into real-world impact.
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