Generative AI and Agentic AI Training Course
Generative AI and Agentic AI are two influential paradigms propelling the next wave of automation and intelligence. The former focuses on content creation, while the latter emphasizes goal-driven, autonomous behavior.
This instructor-led, live training (available online or onsite) is designed for intermediate-level AI and technical professionals who want to gain a deeper understanding of how to build, evaluate, and integrate generative and agentic AI into practical applications.
By the end of this training, participants will be able to:
- Grasp the architecture and capabilities of generative AI systems.
- Examine the emergence of autonomous AI agents and how they enhance large language models (LLMs).
- Utilize prompt engineering and tool integrations for practical deployments.
- Evaluate different models, tools, and techniques for responsible deployment.
Format of the Course
- Interactive lectures and discussions.
- Hands-on use of generative and agentic AI tools in real-world scenarios.
- Guided exercises centered 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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Testimonials (1)
Learned a lot a didn't already know (agents, chat settings)
Sebastien Nolet - Canarie
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