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Training on Generative AI

Categories: Technlogy
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About Course

Generative Artificial Intelligence (GenAI) has rapidly transformed how organizations innovate, create content, and automate tasks by enabling machines to produce entirely new, original outputs rather than simply classifying or analyzing existing data. From marketing teams using AI-generated images and copy to accelerate campaign launches, to software engineers leveraging code-generation tools that boost developer productivity, the impact of GenAI spans virtually every industry. In 2024 alone, global investment in GenAI startups surpassed $20 billion, and leading enterprises report up to 40 percent reductions in content-creation time and 30 percent improvements in prototype development cycles.

Starting with the inner workings of modern generative architectures, we will delve into best practices for prompt design, practical workflows for fine-tuning and evaluation, multimodal integration techniques, and ethical frameworks that safeguard responsible AI adoption. By course end, you’ll have both the conceptual grounding and the practical skill set to lead GenAI initiatives that drive real business value.

Target Participants

This course is ideal for professionals, content creators, data analysts, product managers, and anyone curious about applying generative AI in their workflows.

What You Will Learn

By the end of this course, the participants will be able to:

  • Explain core concepts and architectures underlying generative AI
  • Craft effective prompts to guide text, image, audio, and code generation
  • Customize pre-trained models via fine-tuning for specific tasks
  • Assess and mitigate ethical and bias-related risks in generative outputs
  • Prototype and deploy a simple generative AI application

Course Duration

Online                            7 Days

Classroom-based         5 Days

What Will You Learn?

  • Explain core concepts and architectures underlying generative AI
  • Craft effective prompts to guide text, image, audio, and code generation
  • Customize pre-trained models via fine-tuning for specific tasks
  • Assess and mitigate ethical and bias-related risks in generative outputs
  • Prototype and deploy a simple generative AI application

Course Content

Introduction to Generative AI & Foundations
Evolution from rule-based to generative AI Key GenAI architectures Popular models Use-cases across industries Setting up your development environment

Prompt Engineering & Text Generation
Anatomy of a prompt Zero-, one-, and few-shot prompting Controlling tone, style, and length Chain-of-thought techniques Mitigating hallucinations and bias

Multimodal Generative AI: Images, Audio & Code
Text-to-image generation Speech generation and transcription Code generation tools (e.g., Codex, Copilot) Multimodal integration Demo: Chatbot with image support

Fine-Tuning & Customization
When to fine-tune vs. prompt-tune Data collection and curation Transfer learning Model evaluation techniques Deployment (APIs vs. self-hosting)

Ethics, Governance & Future Trends
Bias, misinformation, deepfakes Data privacy and IP Responsible AI frameworks Latest research directions

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