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Enterprise GenAI & AI Agents: Mastering LLM and RAG for Productivity

Building Intelligent AI Systems for Enterprise Transformation

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Why Choose Us

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.

 

Why Choose Enterprise GenAI & AI Agents: Mastering LLM and RAG for Productivity
Course Goals

What are the Goals?

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:

  • Explain how Large Language Models work including transformers, tokens, and embeddings and evaluate popular LLM platforms and architectures
  • Identify LLM limitations including hallucinations and reliability challenges and evaluate their implications for enterprise deployment
  • Apply prompt engineering principles to design structured prompts that improve accuracy, control, and business task performance
  • Automate workflows using GenAI tools and build productivity assistants across marketing, finance, operations, and HR functions
  • Explain RAG architecture and apply data ingestion, knowledge base preparation, and vector database principles to enterprise knowledge assistant development
  • Reduce hallucinations and improve accuracy in enterprise AI systems using RAG design techniques
  • Explain how AI agents work, evaluate agent frameworks and orchestration tools, and design autonomous task agents
  • Design multi-agent collaboration systems and build a working business AI agent in a practical workshop
  • Apply enterprise system integration, security, privacy, and compliance considerations to GenAI deployment
  • Develop an enterprise AI governance framework and adoption roadmap with ROI measurement and risk mitigation strategies

Who is this Training Course for?

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:

  • AI engineers and data scientists designing and deploying LLM, RAG, and AI agent systems for enterprise use
  • Technology leads and solution architects evaluating GenAI platform options and enterprise integration strategies
  • Digital transformation professionals driving GenAI adoption across business functions and workflow automation
  • Product managers developing AI-powered products that leverage LLM, RAG, or agent capabilities
  • IT and cybersecurity professionals managing security, privacy, and compliance in enterprise GenAI deployments
  • Business analysts and operations professionals applying prompt engineering and GenAI automation to productivity improvement
  • AI governance and risk professionals developing frameworks for responsible enterprise GenAI deployment
  • Graduate technology and data professionals entering enterprise AI roles requiring LLM, RAG, and agent expertise
Learning Approach

How will this Training Course be Presented?

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:

  • Instructor-led sessions covering LLM architecture, prompt engineering, RAG design, agent frameworks, and governance principles
  • LLM platform evaluation sessions examining popular architectures, capabilities, limitations, and enterprise suitability
  • Prompt engineering workshops designing structured prompts for accuracy, consistency, and business task automation across multiple functions
  • Practical AI agent build developing a working business AI agent in a structured hands-on workshop
  • Enterprise deployment and governance sessions applying integration, security, compliance, ROI measurement, and AI adoption roadmap development

The Course Content

Foundations of Generative AI & LLM Ecosystems

  • Overview of AI evolution and generative AI landscape
  • How Large Language Models work (transformers, tokens, embeddings)
  • Popular LLM platforms and architectures
  • Business use cases across industries
  • Limitations, hallucinations, and reliability challenges

Prompt Engineering & Productivity Automation

  • Principles of prompt engineering for business tasks
  • Designing structured prompts for accuracy and control
  • Automating workflows using GenAI tools
  • Building productivity assistants for teams
  • Case studies: marketing, finance, operations, HR 

Retrieval-Augmented Generation (RAG) Systems

  • Understanding RAG architecture and components
  • Data ingestion and knowledge base preparation
  • Vector databases and semantic search
  • Building enterprise knowledge assistants
  • Improving accuracy and reducing hallucinations 

AI Agents for Business Applications

  • What are AI agents and how they work
  • Agent frameworks and orchestration tools
  • Designing autonomous task agents
  • Multi-agent collaboration systems
  • Practical workshop: building a business AI agent 

Deployment, Governance & Implementation Strategy

  • Integrating AI into enterprise systems
  • Security, privacy, and compliance considerations
  • AI governance frameworks and risk mitigation
  • Measuring ROI and performance of AI solutions
  • Developing an enterprise AI adoption roadmap
Recognition

Certificate

  • • Wallstreet Development Academy Certificate of Completion for delegates who attend and complete the training course.
Collaborations

In Partnership With

Anderson Copex Coventry