Udemy - Enterprise GenAI with RAG and Agents

Enterprise GenAI with RAG and Agents
https://WebToolTip.com
Published 8/2026

MP4 | Video: h264, 3840x2160 | Audio: AAC, 44.1 KHz, 2 Ch

Language: English | Duration: 1h 28m | Size: 1.72 GB
Design secure enterprise GenAI systems with RAG, AI agents, governance, monitoring, and production-scale architecture.
What you'll learn

Explain the business drivers behind enterprise generative AI adoption and identify where GenAI can create measurable value.

Understand common enterprise GenAI architecture patterns, deployment models, and integration approaches.

Compare public cloud, private cloud, hybrid, and on-premises deployment considerations.

Identify enterprise knowledge sources suitable for retrieval-augmented generation.

Design RAG pipelines involving document ingestion, chunking, embeddings, indexing, retrieval, and grounded generation.

Apply retrieval, ranking, filtering, and metadata strategies to improve response relevance.

Create prompts that use retrieved enterprise context while reducing unsupported or hallucinated answers.

Explain how AI agents plan tasks, call tools, use memory, and complete multi-step workflows.

Design agentic workflows that coordinate APIs, databases, business applications, and specialized agents.

Select appropriate single-agent, multi-agent, and orchestration patterns for enterprise use cases.

Apply access controls, permissions, data protections, and human approval processes.

Identify risks involving prompt injection, sensitive-data exposure, unauthorized actions, and model misuse.

Align GenAI applications with privacy, security, compliance, audit, and governance requirements.

Evaluate RAG and agent systems using relevance, faithfulness, accuracy, latency, reliability, and business metrics.

Implement monitoring, logging, tracing, observability, fallback, and error-recovery practices.

Develop a phased rollout strategy for moving enterprise GenAI solutions from prototypes to production.
Requirements

Basic familiarity with generative AI, large language models, or ChatGPT is recommended.

A general understanding of RAG, APIs, software applications, or enterprise systems can be helpful.

No advanced machine learning, deep learning, or mathematics knowledge is required.

Basic programming knowledge is useful for technical exercises but is not required to understand the architecture and strategy concepts.

A computer with an internet connection is recommended.

Experience with cloud platforms, databases, security, architecture, or business processes may be beneficial but is not mandatory.

Learners should be comfortable thinking about technology from business, architecture, security, and operational perspectives.

An interest in building scalable, secure, and production-ready enterprise AI systems is the most important prerequisite.