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Duration 14 hours
Course Outline
Introduction to Generative AI and Agentic AI
- Defining Generative AI and Agentic AI
- Understanding their differences and how they synergize
- Industry-wide applications and emerging trends
Generative AI Architecture and Tools
- Transformer-based models: GPT, LLaMA, Claude, and others
- Fine-tuning compared with in-context learning
- Key tools: ChatGPT, Hugging Face Transformers, and Google AI Studio
Prompt Engineering for Control and Structure
- Effective prompt patterns for writing, coding, and summarization
- Techniques including few-shot, zero-shot, and chain-of-thought prompting
- Leveraging prompt libraries and testing utilities
Understanding Agentic AI
- The definition and evolutionary path of agentic AI
- Core architectures: planning, memory, tool use, and self-reflection
- Leading frameworks: AutoGPT, BabyAGI, CrewAI, and LangGraph
Designing and Deploying Autonomous Agents
- Goal definition and task decomposition strategies
- Integrating external tools and APIs for search, memory, and code execution
- Multi-agent coordination and human-in-the-loop oversight
Use Cases and Implementation Scenarios
- Distinguishing between content generation and task orchestration
- Applications in enterprise productivity, customer support, and data extraction
- Ensuring responsible and secure implementation practices
Summary and Next Steps
Requirements
- A foundational understanding of AI and machine learning principles
- Practical experience with APIs or scripting languages, such as Python
- Familiarity with prompt engineering or the utilization of large language models
Target Audience
- AI developers and engineers
- Innovation and R&D teams
- Technical product managers investigating agentic AI systems
Testimonials (1)
the tips and recommended prompts that we can take away from this training
