Generative AI and Agentic AI Training Course
Generative AI and Agentic AI represent two distinct yet powerful paradigms driving the next wave of automation and intelligence—one excels in content creation, while the other focuses on goal-oriented, autonomous behavior.
This instructor-led live training, available both online and on-site, is designed for intermediate-level AI and technical professionals who want to learn how to build, assess, and integrate generative and agentic AI into practical applications.
Upon completing this training, participants will be able to:
- Grasp the architecture and capabilities of generative AI systems.
- Explore the emergence of autonomous AI agents and understand how they extend the capabilities of Large Language Models (LLMs).
- Apply prompt engineering and tool integrations for real-world deployments.
- Compare various models, tools, and techniques to ensure responsible deployment.
Course Format
- Interactive lectures and discussions.
- Hands-on practice with generative and agentic AI tools in real-world scenarios.
- Guided exercises focusing on content generation and autonomous workflows.
Customization Options
- For those interested in a customized training session, please contact us to make arrangements.
Course Outline
Introduction to Generative AI and Agentic AI
- Defining Generative AI and Agentic AI.
- Understanding their differences and complementary nature.
- Exploring industry trends and use cases.
Generative AI Architecture and Tools
- Transformer models: GPT, LLaMA, Claude, and others.
- Comparing fine-tuning with in-context learning.
- Overview of key tools: ChatGPT, Hugging Face Transformers, Google AI Studio.
Prompt Engineering for Control and Structure
- Prompt patterns for writing, coding, summarization, and more.
- Techniques: Few-shot, zero-shot, and chain-of-thought prompting.
- Utilizing prompt libraries and testing tools.
Understanding Agentic AI
- The definition and evolution of agentic AI.
- Core architectures: planning, memory, tool usage, and self-reflection.
- Popular frameworks: AutoGPT, BabyAGI, CrewAI, LangGraph.
Designing and Deploying Autonomous Agents
- Setting goals and decomposing tasks.
- Integrating tools and APIs (search, memory, code execution).
- Managing multi-agent coordination and human-in-the-loop supervision.
Use Cases and Implementation Scenarios
- Distinguishing between content generation and task orchestration.
- Applications in enterprise productivity, customer support, and data extraction.
- Ensuring responsible and secure implementation.
Summary and Next Steps
Requirements
- A foundational understanding of AI and machine learning concepts.
- Experience working with APIs or scripting languages like Python.
- Familiarity with prompt engineering or the usage of large language models.
Target Audience
- AI developers and engineers.
- Innovation and R&D teams.
- Technical product managers looking to explore agentic AI systems.
Open Training Courses require 5+ participants.
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