Generative AI and Agentic AI Training Course
Generative AI and Agentic AI represent two pivotal paradigms propelling the next generation of automation and intelligence. The former centers on creating content, while the latter emphasizes autonomous, goal-oriented behavior.
This instructor-led training session, available both online and onsite, is designed for intermediate-level technical professionals and AI specialists eager to master the development, evaluation, and integration of generative and agentic AI into practical applications.
Upon completion of this training, participants will be capable of:
- Gaining a deep understanding of the architecture and capabilities inherent in generative AI systems.
- Examining the emergence of autonomous AI agents and their role in extending the functionality of Large Language Models (LLMs).
- Leveraging prompt engineering and tool integrations for real-world deployment scenarios.
- Comparing various models, tools, and techniques to ensure responsible AI deployment.
Course Format
- Engaging lectures paired with interactive discussions.
- Practical, hands-on experience with generative and agentic AI tools in realistic scenarios.
- Guided exercises targeting content creation and autonomous workflow management.
Customization Options
- For organizations seeking tailored training for this course, please contact us to arrange a customized solution.
Course Outline
Introduction to Generative AI and Agentic AI
- Defining Generative AI and Agentic AI.
- Distinguishing features and complementary aspects of each approach.
- Industry-wide use cases and current trends.
Generative AI Architecture and Tools
- Transformer-based models: GPT, LLaMA, Claude, and more.
- Fine-tuning techniques versus in-context learning.
- Key tools: ChatGPT, Hugging Face Transformers, Google AI Studio.
Prompt Engineering for Control and Structure
- Effective prompt patterns for writing, coding, summarization, and other tasks.
- Techniques including few-shot, zero-shot, and chain-of-thought prompting.
- Utilizing prompt libraries and testing environments.
Understanding Agentic AI
- Definitions and the evolutionary path of agentic AI.
- Core architectures: planning, memory management, tool usage, and self-reflection.
- Leading frameworks: AutoGPT, BabyAGI, CrewAI, LangGraph.
Designing and Deploying Autonomous Agents
- Setting goals and decomposing complex tasks.
- Integrating tools and APIs for search, memory, and code execution.
- Coordinating multi-agent systems and implementing human-in-the-loop supervision.
Use Cases and Implementation Scenarios
- Differentiating between content generation and task orchestration.
- Applications in enterprise productivity, customer support, and data extraction.
- Strategies for responsible and secure implementation.
Summary and Next Steps
Requirements
- A foundational grasp of artificial intelligence and machine learning principles.
- Practical experience using APIs or scripting languages, particularly Python.
- Familiarity with prompt engineering techniques or the general 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.
Open Training Courses require 5+ participants.
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