Rag Strategy & Execution: Build Enterprise Knowledge Systems

Master the strategy, design, and governance of Retrieval-Augmented Generation to transform enterprise knowledge access

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.

Overview

Section 1: Foundations of RAG – What Every Leader Must Know

Lecture 1 Introduction to Foundations of RAG – What Every Leader Must Know

Lecture 2 What is RAG?

Lecture 3 How RAG Works (Simplified)

Lecture 4 Types of RAG Architectures

Lecture 5 RAG vs. Fine-Tuning vs. Prompt Engineering

Lecture 6 Section 1 Wrap-Up: What You’ve Learned

Section 2: Business Applications and Use Cases

Lecture 7 Introduction to Business Applications and Use Cases

Lecture 8 Common RAG Use Cases Across Industries

Lecture 9 RAG in Your Industry: Case Studies

Lecture 10 Low-Code and No-Code RAG Options for Leaders

Lecture 11 Evaluating RAG Vendors and Implementation Paths

Lecture 12 Section 2 Wrap-Up: Business Applications and Use Cases

Section 3: Data, Governance, and Risk

Lecture 13 Introduction to Data, Governance, and Risk

Lecture 14 What Makes a Good RAG Dataset?

Lecture 15 Governance & Compliance in RAG Systems

Lecture 16 Privacy, IP, and Legal Risks

Lecture 17 Risk Mitigation Strategies

Lecture 18 Section 3 Wrap-Up: Data, Governance, and Risk

Section 4: Strategic Thinking with RAG

Lecture 19 Introduction to Strategic Thinking with RAG

Lecture 20 The RAG Stack: Build or Buy?

Lecture 21 RAG in Enterprise AI Strategy

Lecture 22 RAG and Organizational Change

Lecture 23 Measuring Success: KPIs and Value Metrics

Lecture 24 Section 4 Wrap-Up: Strategic Thinking with RAG

Section 5: The Future of RAG and AI-Augmented Organizations

Lecture 25 Introduction to The Future of RAG and AI-Augmented Organizations

Lecture 26 Multi-Agent Systems + RAG

Lecture 27 RAG + Search + Voice + Vision

Lecture 28 The Road Ahead: Trends and Challenges

Lecture 29 Becoming an AI-Ready Leader

Lecture 30 Section 5 Wrap-Up: The Future of RAG and AI-Augmented Organizations

Section 6: RAG Business Playbook

Lecture 31 RAG Business Playbook: Strategic Deployment Guide

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