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Duration 14 hours
Course Outline
Introduction to Generative and Agentic AI
- Defining Generative AI and Agentic AI
- Distinguishing their differences and synergies
- Industry-specific use cases and emerging trends
Generative AI Architecture & Tooling
- Transformer models: GPT, LLaMA, Claude, and others
- Fine-tuning versus in-context learning approaches
- Key tools: ChatGPT, Hugging Face Transformers, Google AI Studio
Prompt Engineering for Control & Structure
- Prompt patterns for writing, coding, summarization, and more
- Few-shot, zero-shot, and chain-of-thought prompting techniques
- Leveraging 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, LangGraph
Designing & Deploying Autonomous Agents
- Goal setting and task decomposition strategies
- Integrating tools and APIs (search, memory, code execution)
- Multi-agent coordination and human-in-the-loop oversight
Use Cases & Implementation Scenarios
- Content generation versus task orchestration
- Applications in enterprise productivity, customer support, and data extraction
- Principles of responsible and secure implementation
Summary & Next Steps
Requirements
- A foundational understanding of AI and machine learning principles
- Hands-on experience with APIs or scripting languages like 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 exploring agentic AI ecosystems
Testimonials (1)
the tips and recommended prompts that we can take away from this training
