Generative AI and Agentic AI Training Course
Generative AI and Agentic AI represent two transformative paradigms propelling the next generation of automation and intelligence. While generative AI concentrates on creating content, agentic AI is dedicated to achieving goals through autonomous behavior.
This instructor-led live training, available both online and onsite, is designed for intermediate-level AI specialists and technical professionals who want to learn how to build, assess, and incorporate generative and agentic AI solutions into practical applications.
Upon completing this training, participants will be capable of:
- Grasping the architecture and capabilities of generative AI systems.
- Investigating the emergence of autonomous AI agents and their extension of Large Language Models (LLMs).
- Applying prompt engineering and tool integrations for real-world deployments.
- Evaluating models, tools, and techniques to ensure responsible deployment.
Course Format
- Interactive lectures and discussions.
- Practical application of generative and agentic AI tools in realistic scenarios.
- Guided exercises centered on content creation and autonomous workflows.
Customization Options for the Course
- For a tailored training experience, 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 synergies between the two
- Industry use cases and emerging trends
Generative AI Architecture and Tools
- Transformer models: GPT, LLaMA, Claude, and others
- Fine-tuning versus in-context learning
- 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 utilities
Understanding Agentic AI
- Definition and evolution of agentic AI
- Core architectures: planning, memory, tools, and self-reflection
- Leading frameworks: AutoGPT, BabyAGI, CrewAI, LangGraph
Designing and Deploying Autonomous Agents
- Defining goals and decomposing tasks
- Integrating tools and APIs (search, memory, code execution)
- Multi-agent coordination and human-in-the-loop oversight
Use Cases and Implementation Scenarios
- Content generation compared to task orchestration
- Applications in enterprise productivity, customer support, and data extraction
- Ensuring responsible and secure implementation
Summary and Next Steps
Requirements
- Foundational knowledge of AI and machine learning concepts
- Hands-on experience with APIs or scripting languages like Python
- Familiarity with prompt engineering or the use of large language models
Target Audience
- AI developers and engineers
- Innovation and Research & Development (R&D) teams
- Technical product managers interested in exploring agentic AI systems
Open Training Courses require 5+ participants.
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