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Course Outline
Introduction to Generative and Agentic AI
- Defining Generative AI and Agentic AI
- Analyzing the distinctions and synergies between the two
- Key use cases and industry trends
Generative AI Architecture and Tools
- Overview of Transformer models: GPT, LLaMA, Claude, and others
- Comparing fine-tuning with in-context learning
- Essential tools: ChatGPT, Hugging Face Transformers, and Google AI Studio
Prompt Engineering for Control and Structure
- Developing prompt patterns for writing, coding, summarization, and more
- Techniques for few-shot, zero-shot, and chain-of-thought prompting
- Utilizing prompt libraries and testing utilities
Understanding Agentic AI
- The definition and evolution of agentic AI
- Core architectures: planning, memory, tools, and self-reflection
- Leading frameworks: AutoGPT, BabyAGI, CrewAI, and LangGraph
Designing and Deploying Autonomous Agents
- Strategies for goal setting and task decomposition
- Integrating external tools and APIs (search, memory, code execution)
- Managing 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
- Best practices for responsible and secure implementation
Summary and Future Directions
Requirements
- A foundational understanding of AI and machine learning principles
- Hands-on experience with APIs or scripting languages, such as Python
- Prior familiarity with prompt engineering or the utilization of large language models
Target Audience
- AI developers and software engineers
- Innovation and R&D teams
- Technical product managers interested in exploring agentic AI systems
14 Hours
Testimonials (1)
the tips and recommended prompts that we can take away from this training
