Generative AI and Agentic AI Training Course
Generative AI and Agentic AI represent two transformative paradigms driving the next era of automation and intelligence—one excels in content creation, while the other focuses on autonomous, goal-oriented behavior.
This instructor-led, live training (available online or onsite) is designed for intermediate-level AI and technical professionals seeking to understand how to build, assess, and integrate generative and agentic AI solutions into practical applications.
Upon completion 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 their extension of Large Language Models (LLMs).
- Apply prompt engineering and tool integrations for real-world deployments.
- Evaluate models, tools, and techniques to ensure responsible deployment.
Course Format
- Interactive lectures and discussions.
- Hands-on experience with generative and agentic AI tools in practical scenarios.
- Guided exercises centered on content generation and autonomous workflows.
Customization Options
- For customized training requests, please contact us to arrange your session.
Course Outline
Introduction to Generative AI and Agentic AI
- Defining Generative AI and Agentic AI.
- Key differences and complementary strengths.
- 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, and more.
- Few-shot, zero-shot, and chain-of-thought prompting strategies.
- Utilizing prompt libraries and testing tools.
Understanding Agentic AI
- Definition and evolution of agentic AI.
- Architectures: planning, memory, tools, and 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 execution).
- 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
- Fundamental 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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