Design a production-ready RAG architecture for enterprise GenAI applications
Overview
Design a production-ready RAG architecture for enterprise GenAI applications
Most RAG projects look impressive but fail when they meet real enterprise data, messy documents, governance requirements, cost limits, and users who expect accurate answers. THIS workshop shows you how to close that gap.
In this hands-on masterclass, you’ll learn how to design a grounded LLM workflow that turns enterprise data into trusted, traceable answers. You’ll work through the practical decisions that determine whether a RAG system succeeds in production: ingestion, chunking, metadata enrichment, vector search, retrieval tuning, evaluation, governance, and cost control.
You’ll see how to improve answer quality, reduce hallucinations, test failure modes, and make smarter trade-offs across accuracy, latency, and budget. The session also shows where knowledge graphs, ontologies, Microsoft Fabric, and Power BI can strengthen grounding, traceability, and business adoption in real enterprise AI deployments.
By the end, you’ll have a production-ready RAG blueprint you can adapt immediately for your own AI applications.
How this workshop will accelerate your career
By participating in this masterclass, you'll learn how to:
Design a production-ready RAG architecture for enterprise GenAI applications
Build grounded LLM workflows using enterprise data, chunking, metadata enrichment, indexing, and vector search
Optimize retrieval strategies to improve accuracy, performance, and cost efficiency
Evaluate LLM outputs, reduce hallucinations, and implement practical testing frameworks
Apply AI governance, guardrails, and deployment strategies to move GenAI solutions from prototype to production
Why attend now?
Most GenAI projects fail not because of the model itself, but because of poor grounding, weak evaluation, and a lack of production-ready architecture.
As enterprises accelerate AI adoption, demand is growing for professionals who can build reliable, scalable, and governed AI systems. This workshop provides practical, career-relevant skills that can help you stand out in AI engineering, data, analytics, architecture, and enterprise technology roles.
Who should attend?
This workshop is ideal for:
AI Engineers and GenAI practitioners
Data Engineers and analytics professionals
Software Engineers and solution architects
Data Scientists and Machine Learning Engineers
Technology Consultants and enterprise architects
Professionals transitioning into enterprise AI and GenAI roles
Prerequisites
Basic understanding of Generative AI, LLMs, or enterprise data workflows
Familiarity with APIs, databases, or cloud-based data platforms is helpful but not required
Experience in data, analytics, AI, software engineering, or solution architecture will help you maximize your learning
No advanced machine learning or deep learning background is required
An interest in building practical RAG applications, grounded AI systems, or enterprise GenAI solutions
Learn from the best
Brian Bønk is a Data Platform MVP and Microsoft recognized FastTrack Solution Architect with more than two decades of experience in data, analytics, and enterprise platform implementation. Known for bridging business and technology, he helps teams design scalable data solutions that deliver measurable impact. In this masterclass, Brian brings practical expertise in Microsoft Fabric, Power BI, governance, and AI architecture to help learners build grounded, production-ready GenAI systems with confidence and clarity.