Generative AI & Prompt Engineering

Why This Course

Generative AI has redefined the boundaries of creativity, automation, and problem-solving — powering applications from intelligent content generation to design, coding, and decision support.

Understanding how to interact effectively with large language models (LLMs) through prompt engineering is now a critical capability for professionals across industries.

This program provides participants with practical knowledge and hands-on experience in using and optimizing generative AI systems.

It combines technical insight, design thinking, and communication strategy, enabling participants to craft precise, efficient, and impactful prompts for real-world use cases.

What You’ll Learn and Practice

By joining this program, you will:

• Understand the fundamentals and architecture of generative AI models.
• Learn the principles and best practices of effective prompt engineering.
• Gain hands-on experience in building and optimizing generative AI workflows.
• Explore creative and enterprise applications of text, image, and code generation.
• Develop responsible use strategies aligned with ethical and organizational standards.

The Program Flow

Day 1: Introduction to Generative AI

• The evolution and impact of generative AI across industries.
• Understanding model types: GPT, diffusion models, and multimodal AI.
• Key concepts: tokens, embeddings, temperature, and model parameters.
• Overview of text, image, and code generation capabilities.
• Case study: how organizations leverage generative AI for productivity and innovation.

Day 2: Foundations of Prompt Engineering

• The role of prompts in guiding LLM behavior.
• Components of effective prompts: clarity, context, and constraints.
• Prompt patterns: zero-shot, few-shot, and chain-of-thought prompting.
• Common challenges: ambiguity, bias, and response inconsistency.
• Workshop: crafting and refining prompts for creative and analytical tasks.

Day 3: Advanced Prompt Techniques and Workflows

• System, user, and contextual prompts — structuring complex interactions.
• Using prompt templates and role-based instructions for reliability.
• Dynamic prompting with APIs, parameter tuning, and memory integration.
• Combining retrieval with generation (RAG-based prompt design).
• Practical exercise: designing an adaptive prompt system for a real-world use case.

Day 4: Applied Generative AI Use Cases

• Text generation: summarization, ideation, knowledge extraction, and dialogue systems.
• Image and media generation with diffusion models and multimodal frameworks.
• Generative AI in business applications — marketing, education, law, and software development.
• Hands-on project: building a functional generative AI assistant using prompt workflows.
• Group discussion: evaluating creativity, originality, and ethical responsibility.

Day 5: Responsible Use, Evaluation, and Optimization

• Principles of responsible AI: transparency, bias mitigation, and data privacy.
• Evaluating LLM outputs — accuracy, consistency, and trustworthiness.
• Cost optimization and model performance tuning for production use.
• Future trends: autonomous agents, multimodal AI, and hybrid human-AI collaboration.
• Action workshop: developing a generative AI prompt library and governance plan.

Individual Impact

• Gain practical expertise in designing, testing, and refining prompts for LLMs.
• Strengthen creative and analytical thinking through AI-driven collaboration.
• Learn to integrate generative AI tools into professional workflows.
• Build confidence in managing and scaling AI-assisted solutions.
• Develop awareness of ethical and responsible AI usage in content and automation.

Work Impact

• Enhance productivity through structured use of generative AI across teams.
• Foster innovation and knowledge-sharing using prompt-driven AI tools.
• Improve the quality, accuracy, and compliance of AI-generated outputs.
• Build organizational capability in prompt engineering and AI literacy.
• Support safe, ethical, and efficient AI adoption across business functions.

Training Methodology

This program combines interactive learning, experimentation, and applied creativity to ensure deep understanding and practical competence.

Learning methods include:

• Live demonstrations of LLMs and generative AI tools.
• Guided labs for prompt testing and optimization.
• Case studies from leading enterprises and research institutions.
• Group projects and role-based prompt design simulations.
• Toolkits and prompt templates for continued use beyond the course.

Beyond the Course

Upon completion, participants will be equipped to design and deploy effective prompts and generative AI applications that enhance productivity, creativity, and innovation.

They will leave ready to leverage the power of AI responsibly — creating intelligent, efficient, and human-centered solutions across a wide range of professional domains.