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Course Outline
Introduction to Generative AI and Agentic AI
- Defining Generative AI and Agentic AI
- Distinguishing their differences and synergistic relationships
- Key use cases and industry trends
Generative AI Architecture and Tools
- Transformer models: GPT, LLaMA, Claude, and others
- Fine-tuning versus in-context learning strategies
- Essential 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
- Leveraging prompt libraries and testing utilities
Understanding Agentic AI
- Defining the scope and evolution of agentic AI
- Core architectures: planning, memory, tools, 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 oversight
Use Cases and Implementation Scenarios
- Differentiating between content generation and task orchestration
- Applications in enterprise productivity, customer support, and data extraction
- Ensuring responsible and secure deployment practices
Summary and Next Steps
Requirements
- A solid foundation in AI and machine learning concepts
- Practical experience with APIs or scripting languages, such as Python
- Familiarity with prompt engineering and the use of large language models
Target Audience
- AI developers and engineers
- Innovation and R&D teams
- Technical product managers exploring agentic AI systems
14 Hours
Testimonials (1)
the tips and recommended prompts that we can take away from this training
