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The Enterprise GenAI & AI Agents: Mastering LLM and RAG for Productivity Course gives technology, data, and business transformation professionals a comprehensive, technically grounded understanding of enterprise generative AI — covering Large Language Model architecture, prompt engineering, Retrieval-Augmented Generation systems, AI agent design, and the governance and implementation frameworks needed to deploy GenAI at enterprise scale.
Generative AI is moving rapidly from experimentation to enterprise deployment, and the organisations that benefit most are those whose professionals understand how LLMs actually work, how to engineer reliable prompts, how RAG systems reduce hallucinations, and how AI agents can be designed to autonomously complete complex business tasks. That depth of capability is what this course builds.
Across five focused days, delegates progress from LLM fundamentals and prompt engineering through RAG architecture, vector databases, multi-agent systems, and enterprise deployment, culminating in a practical AI agent build and an enterprise AI adoption roadmap. Every day includes hands-on application to ensure learning translates directly into practitioner-level capability.
The Enterprise GenAI & AI Agents: Mastering LLM and RAG for Productivity Course is built for professionals who want to move beyond using GenAI tools and develop the depth of understanding to design, build, and govern enterprise AI systems that deliver real, measurable productivity outcomes.
The Enterprise GenAI & AI Agents: Mastering LLM and RAG for Productivity Course is designed to develop comprehensive enterprise GenAI capability, from LLM fundamentals and prompt engineering through RAG systems, AI agent design, and enterprise deployment governance.
By the end of this course, participants will be able to:
The Enterprise GenAI & AI Agents: Mastering LLM and RAG for Productivity Course is designed for technology, data, and business transformation professionals who are building, evaluating, or governing enterprise GenAI and AI agent solutions.
This course is suitable for:
The Enterprise GenAI & AI Agents: Mastering LLM and RAG for Productivity Course is delivered through a technically structured, progressively building learning approach that moves from LLM foundations and prompt engineering through RAG system design, AI agent development, and enterprise deployment governance. Each day builds directly on the previous, ensuring delegates develop an integrated, end-to-end understanding of enterprise GenAI architecture and deployment.
Hands-on sessions including prompt design exercises, a RAG system walkthrough, and a practical AI agent build are integrated throughout, culminating in an enterprise AI adoption roadmap session.
Delivery methods include:
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Day 1 covers LLM architecture in practical depth, examining how transformers process language, how tokenisation and embeddings represent text, and how popular LLM platforms differ in capability and design. Critically, delegates also examine LLM limitations including hallucinations, reliability challenges, knowledge cutoff constraints, and context window limitations, developing the technical awareness to evaluate LLM suitability for specific enterprise use cases honestly rather than on the basis of vendor marketing.
RAG is an architecture that combines an LLM with a retrieval system, allowing the model to access relevant information from a curated knowledge base rather than relying solely on its training data. This significantly reduces hallucinations, improves factual accuracy, and makes enterprise AI systems genuinely trustworthy for knowledge-intensive applications. Day 3 covers RAG architecture, data ingestion, knowledge base preparation, vector databases, and semantic search, giving delegates the capability to design and build enterprise knowledge assistants that are accurate, auditable, and fit for professional deployment.
Security, privacy, and compliance are addressed within Day 5, covering the specific risks that enterprise GenAI deployments create including data leakage, model poisoning, prompt injection, intellectual property exposure, and regulatory compliance obligations. Delegates develop the practical governance awareness to design GenAI systems that meet enterprise security standards and regulatory requirements, rather than deploying AI tools that create unmanaged risk exposure across the organisation.
Day 2 is dedicated to prompt engineering, covering the principles of designing structured prompts that produce accurate, consistent, and controllable outputs for business applications across marketing, finance, operations, and HR. Delegates apply prompt engineering in practical exercises, building the systematic design capability that distinguishes reliable enterprise AI workflows from the inconsistent outputs that poorly designed prompts produce. This is one of the highest-value practical skills the course develops.
Day 4 covers AI agent design comprehensively, examining what agents are, how agent frameworks and orchestration tools work, how autonomous task agents are designed, and how multi-agent collaboration systems are structured. Delegates build a working business AI agent in a practical workshop, developing the hands-on capability to design agents that can autonomously complete complex multi-step business tasks rather than simply responding to single queries.
AI governance is addressed within Day 5, examining how enterprise GenAI governance frameworks are structured, what risk mitigation strategies are most effective for LLM and agent deployments, how to define acceptable use policies for GenAI across the organisation, and how governance structures ensure accountability when AI systems contribute to business decisions. Delegates leave with the governance design capability to build responsible enterprise AI frameworks rather than deploying GenAI without the oversight structures that sustainable adoption requires.
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