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. While one focuses on content creation, the other emphasizes goal-oriented, autonomous behavior.
This instructor-led live training, available both online and onsite, is designed for intermediate-level AI specialists and technical professionals eager to learn how to build, evaluate, and integrate 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.
- Investigate the emergence of autonomous AI agents and their extension of Large Language Models (LLMs).
- Apply prompt engineering and tool integrations for effective deployment.
- Evaluate various models, tools, and techniques to ensure responsible deployment.
Course Format
- Interactive lectures accompanied by discussions.
- Practical, hands-on experience with generative and agentic AI tools in real-world scenarios.
- Guided exercises concentrating on content generation and autonomous workflows.
Course Customization Options
- To arrange a customized training session for this course, please contact us.
Course Outline
Introduction to Generative AI and Agentic AI
- Defining Generative AI and Agentic AI.
- How these concepts differ and complement each other.
- 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.
- Using 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, data extraction.
- 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 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.
Need help picking the right course?
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Generative AI and Agentic AI Training Course - Enquiry
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