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RAG Strategy & Execution: Build Enterprise Knowledge Systems

4.20
14,431 students
2h 12m
Updated Feb 2026

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

Course 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 You’ll also discover how to mitigate risks related to hallucination, outdated content, data exposure, and compliance gaps.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.

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

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Course Details

  • Level All Levels
  • Lectures 32
  • Duration 2h 12m