Retrieval-Augmented Generation (RAG) is a powerful architecture for grounding large language models in trusted data—but building a RAG system that works reliably in production requires more than connecting a model to a vector database.
This course focuses on the practical best practices that separate experimental prototypes from enterprise-grade AI systems. Participants will learn how to improve retrieval accuracy, reduce hallucinations, optimize chunking strategies, design effective prompts, implement evaluation frameworks, and build guardrails that protect data integrity and user trust. Through architecture reviews, real-world case studies, and guided exercises, you will learn how to design RAG pipelines that are scalable, cost-efficient, secure, and measurable. At the end of the workshop, you will be equipped with a practical checklist and reference architecture for building high-quality RAG systems in production environments
Duration:
Half Day
Course Code: BDT 539
Learning Objectives:
After this course, you will be able to:
AI Practitioners, Solution Architects, Technical Product Managers, Data Teams, and Developers who have basic experience with Retrieval-Augmented Generation and want to improve reliability, scalability, and performance.
Basic understanding of LLMs and familiarity with RAG concepts. Experience with any RAG tool (n8n, LangChain, LlamaIndex, etc.) is helpful but not required.
Training material provided: Yes (Digital format)
Hands-on Lab: Instructions will be provided to set up n8n and API keys.
