Piotr Kalota
Piotr Kalota is a Machine Learning Engineer at FELD M with a Master’s in Human-Centered AI from DTU. Specializing in NLP and accessible tech, he develops retrieval-augmented generation (RAG) systems and other LLM-driven solutions. With four years of experience in software engineering and machine learning, he combines human-centered design and innovation to create accessible AI solutions.
Session
Retrieval-Augmented Generation (RAG) systems are transforming how we interact with unstructured data using Large Language Models (LLMs). While it’s now relatively easy to stand up a basic RAG prototype, deploying a robust, customizable, and production-ready system remains challenging.
In this talk, we present our open-source RAG blueprint through the lens of a real-world application: Bundestag Chat—a system that enables users to explore and converse with German parliamentary speeches. We’ll demonstrate how the blueprint streamlined development and scaling, and how its modular architecture allowed for seamless integration of components like LlamaIndex, Hugging Face embeddings, PGVector, Langfuse, and Ragas.
Attendees will walk away with practical insights into customizing RAG pipelines for real use cases, whether building internal tools or user-facing applications. We’ll also explore build-vs-buy trade-offs, retrieval and scaling strategies, and considerations around privacy, evaluation, and monitoring.