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What You'll Learn

Identify high-value business use cases for RAG across teams and workflows
Design a modular
scalable RAG stack for enterprise deployment
Build a content strategy for sourcing
chunking
and indexing knowledge
Establish governance practices for access
traceability
and compliance
Evaluate RAG vendors based on privacy
control
and integration options
Mitigate risks like hallucination
bias
and data exposure in RAG systems
Track and report KPIs that measure RAG’s business impact and trust
Craft a long-term RAG vision aligned with AI agents and automation

Requirements

No coding or AI development experience required — this course is designed for strategic thinkers
not engineers
A basic understanding of how your organization uses internal documents
knowledge bases
and workflows
Familiarity with enterprise tools like SharePoint
Notion
Google Workspace
or CRMs (helpful but not mandatory)
Ideal for those in management
IT
operations
legal
or innovation roles
Curiosity and openness to exploring how AI can enhance decision-making and knowledge access across the enterprise

Description

In today’s fast-moving, data-rich enterprises, static knowledge systems are no longer enough. Enter Retrieval-Augmented Generation (RAG) — a powerful AI technique that enables organizations to unlock the full value of their internal documents, policies, and processes. In this course, RAG Strategy & Execution: Building Enterprise Knowledge Systems, you’ll learn how to move beyond chatbots and pilots to deploy RAG as enterprise-grade infrastructure.

This course is designed for leaders, strategists, and functional decision-makers who want to understand not just how RAG works, but how to make it work within their organization. You’ll explore the complete lifecycle of a RAG system, from use case prioritization to data sourcing, governance, risk management, and performance measurement. Whether you're a CIO planning for GenAI at scale or a business unit leader solving knowledge bottlenecks, this course will give you the blueprint to lead confidently.

You’ll start by identifying the highest-value RAG use cases across business functions like HR, legal, support, and operations. Then, you’ll learn how to prepare RAG-ready datasets — including strategies for document chunking, metadata tagging, and source control. The course walks you through the design of a modular RAG stack, with comparisons of build vs. buy vs. hybrid architectures. You’ll evaluate popular tools like LangChain, ChromaDB, and Ollama, as well as commercial platforms like Glean, Hebbia, and Chatbase.

A major focus of this course is RAG governance. You’ll learn how to implement version control, document-level access rules, output disclaimers, and human-in-the-loop (HITL) validation. You’ll also discover how to mitigate risks related to hallucination, outdated content, data exposure, and compliance gaps.

To help you make confident decisions, we include a full vendor evaluation framework, a detailed risk assessment plan, and a dashboard of RAG performance KPIs — including adoption, trust, and business impact. You’ll also gain insights into emerging trends like multi-agent RAG systems, voice and vision interfaces, and Retrieval-as-a-Service (RaaS).

By the end of this course, you’ll have a complete RAG Business Playbook tailored to your organization — and the leadership mindset to scale AI with trust and purpose. You'll wrap up with a capstone assignment: a two-page vision paper on how your company can leverage RAG to build competitive advantage by 2030.

If you’re serious about AI augmentation, knowledge workflows, and strategic AI governance, this course will give you the clarity, tools, and confidence to lead. Whether you’re a business leader, product manager, innovation officer, or advisor, this is your roadmap to RAG mastery.

Who this course is for:

Business and Functional Leaders (e.g.
in HR
Legal
Ops
Finance) looking to unlock the value of organizational knowledge through AI
Enterprise Architects and IT Strategists who want to design and scale RAG infrastructure
AI and Innovation Officers tasked with operationalizing GenAI in real-world
trustworthy ways
Data Governance and Compliance Professionals who need to evaluate AI systems for risk
traceability
and accountability
Team Leads and Product Managers seeking to integrate RAG into workflows
agents
and user-facing apps
Consultants and Advisors helping organizations plan or pilot RAG-enabled systems
Tech-Savvy Executives and CIOs who need to understand the business impact
architecture
and evolution of RAG
AI Enthusiasts and Career Switchers aiming to upskill in applied GenAI strategy and enterprise deployment