Generative AI and Agentic AI Training Course
Generative AI and Agentic AI represent two potent paradigms propelling the next era of automation and intelligence — one centered on content creation and the other on goal-oriented, self-directed behavior.
This instructor-led training session (delivered online or in-person) is tailored for intermediate-level professionals in AI and technology who aim to grasp how to construct, assess, and incorporate generative and agentic AI into practical applications.
Upon completion of this course, participants will be able to:
- Comprehend the architecture and functionalities of generative AI systems.
- Investigate the emergence of autonomous AI agents and their extension of LLMs.
- Leverage prompt engineering and tool integrations for effective deployments.
- Evaluate models, tools, and techniques to ensure responsible deployment.
Course Format
- Interactive lectures and discussions.
- Practical use of generative and agentic AI tools in real-world situations.
- Guided exercises focused on content creation and autonomous workflows.
Customization Options for the Course
- To request a customized training program, 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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