Description

Ready to make AI systems work with your organization’s unique knowledge and data? Most AI implementations fail because they cannot effectively access and process enterprise information. This course helps you overcome that challenge by mastering data pipelines, gen AI and retrieval-augmented generation (RAG) systems that connect AI models with real-world data.You will learn what retrieval augmented generation (RAG) is and how retrieval augmented generation works, while building systems that transform raw enterprise data into intelligent, context-aware responses. This course turns you into an AI engineer capable of designing scalable RAG pipelines and advanced AI automation workflows.You’ll master data pipeline engineering, including data warehouse pipeline design, document processing, and transforming unstructured data into AI-ready formats. You will also explore data pipeline vs warehouse concepts and understand the meaning of data pipeline in enterprise AI systems.This comprehensive program provides a practical approach to retrieval augmented generation systems, covering RAG architecture, embeddings, vector databases, and intelligent retrieval strategies. You’ll also learn what a RAG pipeline is, what RAG is in GenAI, and how to implement RAG AI systems for real-world applications.Through hands-on labs, you will build production-ready retrieval augmented generation software with adaptive orchestration, personalization, and monitoring. You’ll explore agentic AI workflows and understand what RAG agents are, enabling intelligent and scalable knowledge systems.You will also gain expertise in:Designing enterprise-grade data pipelines for AI-ready processingImplementing retrieval-augmented generation with vector search and embeddingsOptimizing RAG pipelines with reranking, metadata filtering, and adaptive strategiesIntegrating large language models (LLMs) into AI engineering workflowsApplying AI automation and prompt engineering for high-quality outputsBy the end of this course, you will confidently design and deploy end-to-end RAG systems that transform how organizations access and use knowledge. You will build scalable systems capable of handling millions of documents and delivering precise, context-aware responses.Learning ApproachThis course follows a learn-by-doing model:Conceptual lectures covering RAG fundamentals and best practicesHands-on labs for building data pipelines and RAG architecturesQuizzes to reinforce concepts and assess understandingCapstone project to implement a full retrieval augmented generation pipelineMain OutcomeLearners will be able to architect and deploy end-to-end retrieval-augmented generation (RAG) systems integrated with advanced data pipelines, vector databases, and intelligent retrieval strategies.Learning ObjectivesBuild enterprise-grade data pipelines with validation and AI-ready transformationImplement advanced RAG architecture and vector search systemsOptimize retrieval augmented generation pipelines for performance and scalabilityDevelop real-world RAG AI applications for customer support and knowledge systemsApply prompt engineering for LLM optimizationKey TakeawaysEnterprise data pipeline engineering for generative AIProduction-ready retrieval-augmented generation systemsVector database design and semantic searchIntelligent knowledge management using RAG AIAdvanced AI engineering and prompt optimizationSkills GainedAI Data Pipeline EngineeringAdvanced RAG System DevelopmentVector Database ArchitectureIntelligent Knowledge SystemsPrompt Engineering for RAG LLM ApplicationsEnrol NowTake the next step in your AI engineering journey. Master data pipelines and retrieval-augmented generation (RAG) – the most in-demand skills in modern artificial intelligence.Build intelligent systems, advance your career, and become the expert organizations need to unlock the full potential of their data.