RAG systems are easy to demo but hard to make reliably useful. The gap between prototype and real performance is rarely the model, but context quality. Retrieval, chunking, ranking, and context construction shape system behavior, and weaknesses in these layers lead to irrelevant answers, poor grounding, and overconfident outputs, even with strong models. This talk explores practical ways to improve RAG systems: better retrieval signals, cleaner context assembly, noise reduction, and aligning context with user intent.
Mesut has been in IT for around two decades, and currently working as Principal Data Architect at Thoughtworks. He served in different roles leading multiple cross functional teams and technical leads with experience in Mobility, Automotive, Digital Transformation and more areas with a wide range of technology stack from on-prem to cloud solutions, architecture and modern development practices.
