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Duration 14 hours
Course Outline
Overview of Generative and Agentic AI
- Defining Generative AI and Agentic AI
- Distinctions and synergies between the two
- Industry-specific use cases and emerging trends
Generative AI: Architecture and Toolset
- Transformer models including GPT, LLaMA, Claude, and others
- Fine-tuning compared to in-context learning
- Key tools: ChatGPT, Hugging Face Transformers, Google AI Studio
Prompt Engineering: Control and Structure
- Prompt patterns for writing, coding, summarization, and more
- Techniques including few-shot, zero-shot, and chain-of-thought prompting
- Utilizing prompt libraries and testing utilities
The Landscape of Agentic AI
- Defining and tracing the evolution of agentic AI
- Core architectures: planning, memory, tools, and self-reflection
- Leading frameworks: AutoGPT, BabyAGI, CrewAI, LangGraph
Designing and Launching Autonomous Agents
- Establishing goals and breaking down tasks
- Connecting tools and APIs for search, memory, and code execution
- Coordinating multi-agent systems and human-in-the-loop oversight
Real-World Applications and Implementation
- Contrasting content generation with task orchestration
- Applications in enterprise productivity, customer support, and data extraction
- Ensuring responsible and secure deployment practices
Conclusion and Path Forward
Requirements
- A foundational understanding of AI and machine learning principles
- Experience with APIs or scripting languages like Python
- Familiarity with prompt engineering or the application 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
