Generative AI for Technical Managers (GAITM)


Course Overview

Generative AI has emerged as a powerful tool for creating new and innovative solutions in various industries. This course is designed to provide technical managers with a comprehensive understanding of generative AI techniques and their applications. Participants will gain insights into the underlying principles, practical implementation, and management considerations associated with generative AI projects. Through a combination of theoretical lectures, case studies, and hands-on exercises, participants will develop the necessary knowledge and skills to effectively lead generative AI initiatives within their organizations.

Who should attend

The “Generative AI for Technical Managers” course is designed specifically for technical managers who are looking to enhance their knowledge and skills in the field of generative AI.


  • Basic understanding of artificial intelligence concepts and technologies.
  • Familiarity with programming fundamentals and basic coding experience.
  • Prior experience in a technical or managerial role.

Outline: Generative AI for Technical Managers (GAITM)

Generative AI Fundamentals

  • What is Generative AI
  • Key Concepts in Generative AI
  • Generative Models and Discriminative Models
  • Types of Generative Models (e.g., Variational Autoencoders, Generative Adversarial Networks)
  • Training and Inference in Generative Models
  • Discuss few Industry use cases of Generative AI Applications
    • Applications of Generative AI and Deep Learning
      • Image and Video Generation
      • Music and Audio Generation
  • Text Generation
  • Lab: Hands on lab on Text Generation using Large Language models

Introduction to Large Language Models (LLMs)

  • What are Large Language Models?
  • Importance and Applications of Large Language Models
  • Overview of LLMs in the Context of Natural Language Processing
  • Understanding the Architecture of Large Language Models
    • Transformer Architecture
    • Self-Attention Mechanism
    • Pre-training and Fine-tuning of LLMs
  • Training and Data Requirements for Large Language Models
    • Training Corpus and Data Collection
    • Pre-processing and Tokenization
    • Training Process and Computational Resources

Prompt Engineering

  • What is Prompt Engineering?
  • Importance of Prompt Engineering in Modern Organizations
  • Role of Managers in Prompt Engineering and Management
  • Understanding the Prompt Generation Process
  • Design and optimize prompts
    • Apply advanced prompt engineering techniques
    • Review and apply the latest and most advanced prompt engineering techniques
    • Understanding of Multi-modal LLM and different methods in Multi-modal LLMs
    • Tree-of-thought and chain-of-thought methods

Collaborating with Analysts, Engineers, and Scientists

  • Generative AI Product Development
    • Building AI first Products
    • Understanding the complexity and challenges
    • Design Exploration and Ideation
    • Simulation and Testing
  • Generative AI Project Lifecycle
    • Evaluation metrics for generative AI models
    • Qualitative and quantitative assessment of generative AI outputs
    • User feedback and engagement analysis
    • Continual improvement and iteration techniques
  • Data Protection, Privacy and Security
    • Things to consider for protecting Data
    • Data Lifecycle Management
    • Compliances & Regulations
    • Aspects to consider for Data Security
    • Data Privacy Considerations
  • Generative AI Deployment
    • Model deployment strategies: on-premises, cloud-based, and edge deployment
    • Integration with existing systems and workflows
    • Testing and performance optimization
    • Monitoring and maintenance of generative AI models
  • Responsible AI Considerations
    • Biases
    • Ethical implications of generative AI
    • Fairness, transparency, and accountability in AI projects
    • Regulatory frameworks and guidelines for generative AI
    • Building responsible and ethical generative AI systems
  • Understanding the roles and responsibilities of analysts, engineers, and scientists in generative AI projects
  • Effective communication and collaboration strategies
  • Project scoping and requirement gathering
  • Overcoming challenges and mitigating risks in project implementation

Prices & Delivery methods

Online Training

2 days

  • on request
Classroom Training

2 days

  • on request


Currently there are no training dates scheduled for this course.