Generative AI and Agentic AI Training Course
Generative AI and Agentic AI represent two potent paradigms propelling the next generation of automation and intelligence—one dedicated to content creation and the other to autonomous, goal-oriented behavior.
This instructor-led live training, available both online and onsite, is designed for intermediate-level AI and technical professionals aiming to master the development, evaluation, and integration of generative and agentic AI within real-world applications.
Upon completing this training, participants will be equipped to:
- Grasp the architecture and capabilities of generative AI systems.
- Explore the emergence of autonomous AI agents and their extension of Large Language Models (LLMs).
- Apply prompt engineering and tool integrations for practical deployments.
- Evaluate various models, tools, and techniques to ensure responsible deployment.
Course Format
- Interactive lectures and discussions.
- Hands-on application of generative and agentic AI tools in real-world scenarios.
- Guided exercises centered on content generation and autonomous workflows.
Customization Options
- For customized training requests, please contact us to arrange your needs.
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 current 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 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 practices.
Summary and Next Steps
Requirements
- A foundational understanding of AI and machine learning concepts.
- Experience working 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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