Generative AI and Agentic AI Training Course
Generative AI and Agentic AI represent two transformative paradigms shaping the future of automation and intelligence—one dedicated to content creation and the other to autonomous, goal-oriented action.
This instructor-led live training, available online or onsite, is designed for intermediate AI and technical professionals seeking to comprehend, build, evaluate, 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 role in extending Large Language Models (LLMs).
- Apply prompt engineering and tool integrations for real-world implementation.
- Evaluate various models, tools, and techniques to ensure responsible deployment.
Course Format
- Interactive lectures paired with group discussions.
- Practical application of generative and agentic AI tools in authentic business scenarios.
- Guided exercises focusing on content generation and autonomous workflow design.
Course Customization Options
- For personalized training arrangements, please contact us to discuss your specific requirements.
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.
- Distinctions between fine-tuning and in-context learning.
- Essential tools: ChatGPT, Hugging Face Transformers, Google AI Studio.
Prompt Engineering for Control and Structure
- Prompt patterns for writing, coding, summarization, and more.
- Techniques including few-shot, zero-shot, and chain-of-thought prompting.
- Utilizing prompt libraries and testing tools.
Understanding Agentic AI
- Definition and historical evolution of agentic AI.
- Core architectures: planning, memory, tool use, and self-reflection.
- Leading frameworks: AutoGPT, BabyAGI, CrewAI, LangGraph.
Designing and Deploying Autonomous Agents
- Goal setting and task decomposition strategies.
- Integration of tools and APIs (search, memory, code execution).
- Multi-agent coordination and human-in-the-loop supervision.
Use Cases and Implementation Scenarios
- Comparing content generation with task orchestration.
- Applications in enterprise productivity, customer support, and data extraction.
- Ensuring responsible and secure implementation practices.
Summary and Next Steps
Requirements
- Foundational knowledge of AI and machine learning concepts.
- Practical experience with APIs or scripting languages such as Python.
- Familiarity with prompt engineering techniques or the usage of large language models.
Audience
- AI developers and engineers.
- Innovation and R&D teams.
- Technical product managers investigating agentic AI systems.
Open Training Courses require 5+ participants.
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