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Duration 14 hours
Course Outline
Introduction to Generative AI and Agentic AI
- Defining Generative AI and Agentic AI
- Distinctions and synergies between the two paradigms
- Industry-wide use cases and emerging trends
Generative AI Architecture and Tools
- Transformer models: GPT, LLaMA, Claude, and others
- Fine-tuning vs. in-context learning
- Key tools: ChatGPT, Hugging Face Transformers, Google AI Studio
Prompt Engineering for Control and Structure
- Prompt patterns for writing, coding, summarization, etc.
- Few-shot, zero-shot, and chain-of-thought prompting
- Leveraging prompt libraries and testing utilities
Understanding Agentic AI
- The definition and evolution of agentic AI
- Core architectures: planning, memory, tools, self-reflection
- Leading frameworks: AutoGPT, BabyAGI, CrewAI, LangGraph
Designing and Deploying Autonomous Agents
- Establishing goals and decomposing tasks
- Integrating tools and APIs (search, memory, code)
- Multi-agent coordination and human-in-the-loop oversight
Use Cases and Implementation Scenarios
- Contrasting content generation with task orchestration
- Applications in enterprise productivity, customer support, data extraction
- Ensuring responsible and secure implementation
Summary and Next Steps
Requirements
- A solid grasp of AI and machine learning fundamentals
- Practical experience with APIs or scripting languages like Python
- Working knowledge of prompt engineering or leveraging large language models
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
