Generative AI and Agentic AI Training Course
Generative AI and Agentic AI are two powerful paradigms driving the next wave of automation and intelligence — one focused on content generation and the other on goal-driven, autonomous behavior.
This instructor-led, live training (online or onsite) is aimed at intermediate-level AI and technical professionals who wish to understand how to build, evaluate, and integrate generative and agentic AI into real-world applications.
By the end of this training, participants will be able to:
- Understand the architecture and capabilities of generative AI systems.
- Explore the rise of autonomous AI agents and how they extend LLMs.
- Use prompt engineering and tool integrations for practical deployments.
- Compare models, tools, and techniques for responsible deployment.
Format of the Course
- Interactive lecture and discussion.
- Hands-on use of generative and agentic AI tools in real-world scenarios.
- Guided exercises focused on content generation and autonomous workflows.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Course Outline
Introduction to Generative AI and Agentic AI
- What is Generative AI? What is Agentic AI?
- How they differ and complement each other
- Use cases and trends across industries
Generative AI Architecture and Tools
- Transformer models: GPT, LLaMA, Claude, and others
- Fine-tuning vs. in-context learning
- Tools: ChatGPT, Hugging Face Transformers, Google AI Studio
Prompt Engineering for Control and Structure
- Prompt patterns for writing, coding, summarization, etc.
- Few-shot, zero-shot, and chain-of-thought prompting
- Using prompt libraries and testing tools
Understanding Agentic AI
- Definition and evolution of agentic AI
- Architectures: planning, memory, tools, self-reflection
- Popular frameworks: AutoGPT, BabyAGI, CrewAI, LangGraph
Designing and Deploying Autonomous Agents
- Goal setting and task decomposition
- Integrating tools and APIs (search, memory, code)
- Multi-agent coordination and human-in-the-loop supervision
Use Cases and Implementation Scenarios
- Content generation vs. task orchestration
- Enterprise productivity, customer support, data extraction
- Responsible and secure implementation
Summary and Next Steps
Requirements
- An understanding of AI and machine learning concepts
- Experience working with APIs or scripting languages such as Python
- Familiarity with prompt engineering or large language model usage
Audience
- AI developers and engineers
- Innovation and R&D teams
- Technical product managers exploring agentic AI systems
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