Generative AI and Agentic AI Training Course
Generative AI and Agentic AI represent two transformative paradigms fueling the next generation of automation and intelligence—one centered on content creation and the other on autonomous, goal-oriented actions.
This live, instructor-led training (available online or onsite) is designed for intermediate-level AI specialists and technical professionals aiming to master the construction, assessment, and integration of generative and agentic AI solutions into practical applications.
Upon completing this training, participants will be equipped to:
- Grasp the architecture and capabilities of generative AI systems.
- Investigate the emergence of autonomous AI agents and their extension of Large Language Models (LLMs).
- Leverage prompt engineering and tool integrations for effective deployments.
- Evaluate various models, tools, and methodologies for responsible implementation.
Course Format
- Interactive lectures and discussions.
- Practical application of generative and agentic AI tools in real-world scenarios.
- Guided exercises focused on content generation and autonomous workflows.
Course Customization Options
- For inquiries regarding customized training for this course, please contact us to arrange details.
Course Outline
Introduction to Generative AI and Agentic AI
- Defining Generative AI and Agentic AI
- Understanding their differences and complementary roles
- Industry use cases and emerging trends
Generative AI Architecture and Tools
- Transformer models: GPT, LLaMA, Claude, and others
- Fine-tuning versus 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
- Utilizing 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 versus task orchestration
- Enterprise productivity, customer support, data extraction
- Responsible and secure implementation
Summary and Next Steps
Requirements
- A foundational understanding of AI and machine learning concepts
- Practical experience with APIs or scripting languages such as Python
- Familiarity with prompt engineering or the usage of large language models
Audience
- AI developers and engineers
- Innovation and R&D teams
- Technical product managers exploring agentic AI systems
Open Training Courses require 5+ participants.
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