[100% Off] Enterprise Genai With Rag And Agents
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.
Description
This course contains the use of artificial intelligence.
Enterprise GenAI with RAG and Agents is a practical course designed to help professionals understand how to architect, secure, evaluate, and scale enterprise generative AI applications. The course connects large language models with organizational knowledge, external tools, business systems, governance controls, and production operations.
You will begin by exploring the enterprise GenAI landscape, including the business drivers behind adoption, common architecture patterns, and available deployment models. You will learn how organizations can choose between public cloud, private cloud, hybrid, and on-premises approaches based on data sensitivity, integration needs, cost, control, and scalability.
The course then examines retrieval-augmented generation, commonly known as RAG. You will learn how enterprise documents, databases, policies, knowledge bases, and other trusted information sources can be connected to large language models. Topics include document ingestion, chunking, metadata, embeddings, vector search, retrieval design, ranking, filtering, and response grounding.
You will learn how effective enterprise RAG systems improve accuracy by providing relevant context before an answer is generated. The course also explains citation handling, source traceability, retrieval evaluation, and techniques for reducing hallucinations when the available information is incomplete.
The agentic-workflow section introduces AI agents, planning, tool calling, orchestration, and multi-step automation. You will examine how agents can retrieve information, call APIs, interact with databases, coordinate specialized capabilities, and complete business processes. You will also compare single-agent, multi-agent, and orchestrated workflow patterns.
Enterprise AI requires strong governance and security. You will explore access controls, permissions, sensitive-data protection, prompt injection, unauthorized tool use, human approvals, logging, auditability, and compliance requirements. The course emphasizes designing security and governance into the architecture instead of adding them after development.
The final section focuses on production GenAI systems. You will learn how to evaluate retrieval quality, answer faithfulness, agent behavior, latency, reliability, and business outcomes. You will also explore monitoring, tracing, observability, fallback strategies, error recovery, capacity planning, and operational support.
A phased rollout approach will help you move from experiments and proofs of concept to controlled pilots and enterprise deployment. You will learn how to define success criteria, manage risk, gather feedback, and expand adoption responsibly.
By the end of Enterprise GenAI with RAG and Agents, you will understand how to design scalable GenAI architectures, build grounded knowledge applications, create agentic workflows, apply enterprise security controls, and prepare systems for reliable production use.
This course is ideal for architects, AI engineers, developers, technology leaders, product managers, governance teams, security professionals, consultants, and anyone responsible for building or scaling enterprise AI solutions.
