2025-12-10 –, Machine Learning & AI
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.
Retrieval-Augmented Generation (RAG) systems are among the most impactful applications of LLMs, allowing for intelligent querying and contextual understanding of unstructured data. However, turning a prototype into a polished, scalable product is often where complexity sets in.
In this talk, we walk through how our open-source RAG blueprint was used to create Bundestag Chat—a system that allows users to interact with over a decade of German parliamentary debates via a chat interface. This real-world use case illustrates the key benefits of our blueprint: modularity, observability, evaluation, and scalability.
Our architecture includes:
- LlamaIndex for document parsing and chunking,
- Hugging Face embedding models stored in a PGVector vector database,
- Chainlit for an intuitive chat UI,
- Langfuse for logging, observability, and feedback collection,
- Ragas for evaluating response quality across dimensions like faithfulness and relevance.
What made this system successful was the flexibility to swap components, configure data flows, and monitor performance from day one. This modular design made it straightforward to go from an initial prototype to a system deployed in a privacy-sensitive environment.
We’ll also contrast open-source and commercial RAG stacks, sharing insights on when to build versus buy. Topics include:
- Estimating system requirements across different workloads,
- Evaluating model performance and output reliability,
- Ensuring data privacy and legal compliance,
- Gathering and acting on human feedback to improve quality.
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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.
Dr. Matthias Böck holds a doctorate in bioinformatics and machine learning and has been working as a data scientist in the Data Product department at the Munich-based consultancy FELD M since 2013. He is the technical manager for projects in the fields of machine learning and data strategy. He is the author of specialist books on AI, holds design thinking workshops and works with universities on research projects. In addition to these fields, he is also involved in the topic of data for good and its use in practice.