Generative AI and Agentic AI Training Course
Generative AI and Agentic AI represent two pivotal paradigms fueling the next generation of automation and intelligence—one specializing in content creation, while the other emphasizes goal-oriented, autonomous actions.
This instructor-led training, available either online or on-site, is designed for AI and technical professionals with an intermediate level of expertise who want to learn how to construct, assess, and incorporate generative and agentic AI into practical applications.
Upon completing this training, participants will be equipped to:
- Grasp the architecture and capabilities of generative AI systems.
- Examine the emergence of autonomous AI agents and their role in extending Large Language Models (LLMs).
- Apply prompt engineering and tool integrations for real-world implementations.
- Evaluate models, tools, and methodologies for responsible deployment.
Course Format
- Engaging lectures and interactive discussions.
- Practical application of generative and agentic AI tools in realistic scenarios.
- Guided exercises centered on content creation and autonomous workflows.
Customization Options
- To arrange customized training for this course, please get in touch with us.
Course Outline
Introduction to Generative AI and Agentic AI
- Defining Generative AI and Agentic AI.
- Key differences and complementary aspects.
- 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, and more.
- Few-shot, zero-shot, and chain-of-thought prompting techniques.
- 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, and data extraction.
- Responsible and secure implementation strategies.
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.
Target Audience
- AI developers and engineers.
- Innovation and Research & Development (R&D) teams.
- Technical product managers exploring agentic AI systems.
Open Training Courses require 5+ participants.
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