Generative AI and Agentic AI Training Course
Generative AI and Agentic AI represent two transformative paradigms fueling the next era of automation and intelligence. While one focuses on creating new content, the other is driven by autonomous, goal-oriented behavior.
This instructor-led, live training session, available both online and onsite, is designed for intermediate-level AI professionals and technical experts who want to learn how to build, assess, and integrate generative and agentic AI solutions into practical applications.
Upon completion of this training, participants will be able to:
- Comprehend 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 techniques and tool integrations for effective deployments.
- Evaluate models, tools, and methodologies to ensure responsible deployment.
Course Format
- Interactive lectures and group discussions.
- Practical, hands-on experience with generative and agentic AI tools in real-world contexts.
- Guided exercises emphasizing content generation and autonomous workflow design.
Customization Options
- For those interested in a tailored training experience, please contact us to discuss customization possibilities.
Course Outline
Introduction to Generative and Agentic AI
- Defining Generative AI and Agentic AI.
- Key differences and complementary aspects.
- Industry use cases and current trends.
Generative AI Architecture and Tools
- Transformer models including GPT, LLaMA, Claude, and others.
- Distinguishing between fine-tuning and 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 including 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.
- Prominent frameworks: AutoGPT, BabyAGI, CrewAI, LangGraph.
Designing and Deploying Autonomous Agents
- Goal setting and task decomposition strategies.
- Integrating tools and APIs for search, memory, and code execution.
- Multi-agent coordination and human-in-the-loop supervision.
Use Cases and Implementation Scenarios
- Contrasting content generation with task orchestration.
- Applications in enterprise productivity, customer support, and data extraction.
- Principles of responsible and secure implementation.
Summary and Next Steps
Requirements
- A foundational understanding of AI and machine learning concepts.
- Hands-on experience with APIs or scripting languages, particularly Python.
- Familiarity with prompt engineering or the usage of large language models.
Target Audience
- AI developers and engineers.
- Innovation and Research & Development (R&D) teams.
- Technical product managers investigating agentic AI systems.
Need help picking the right course?
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