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Duration 14 hours
Course Outline
Introduction to Generative AI and Agentic AI
- Defining Generative AI and Agentic AI
- Differences and synergies between the two
- Industry-specific use cases and emerging trends
Architecture and Tools for Generative AI
- Transformer-based models: GPT, LLaMA, Claude, and others
- Comparing fine-tuning and in-context learning
- Key tools: ChatGPT, Hugging Face Transformers, and Google AI Studio
Prompt Engineering for Precision and Structure
- Developing 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
Exploring Agentic AI
- Definition and the evolution of agentic AI
- Core architectures: planning, memory, tool usage, and self-reflection
- Leading frameworks: AutoGPT, BabyAGI, CrewAI, and LangGraph
Design and Deployment of Autonomous Agents
- Setting goals and decomposing tasks
- Integrating external tools and APIs (search, memory, code execution)
- Coordinating multi-agent systems and implementing human-in-the-loop oversight
Practical Use Cases and Implementation Scenarios
- Differentiating content generation from task orchestration
- Applications in enterprise productivity, customer support, and data extraction
- Strategies for responsible and secure implementation
Summary and Future Directions
Requirements
- Foundational knowledge of AI and machine learning principles
- Proficiency in working with APIs or scripting languages like Python
- Basic familiarity with prompt engineering or the usage of large language models
Target Audience
- AI developers and software engineers
- Innovation and R&D teams
- Technical product managers interested in agentic AI systems
Testimonials (1)
the tips and recommended prompts that we can take away from this training
