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Duration 14 hours
Course Outline
Introduction to Generative AI and Agentic AI
- Defining Generative AI and Agentic AI
- Analyzing their differences and complementary strengths
- Key use cases and emerging trends across various industries
Generative AI Architecture and Tools
- Transformer models: GPT, LLaMA, Claude, and alternatives
- Distinguishing between fine-tuning and in-context learning
- Essential tools: ChatGPT, Hugging Face Transformers, Google AI Studio
Prompt Engineering for Control and Structure
- Effective prompt patterns for writing, coding, summarization, and more
- Techniques for few-shot, zero-shot, and chain-of-thought prompting
- Leveraging prompt libraries and testing utilities
Understanding Agentic AI
- Core definitions and the evolution of agentic AI
- Architectural components: planning, memory, tool usage, and self-reflection
- Leading frameworks: AutoGPT, BabyAGI, CrewAI, LangGraph
Designing and Deploying Autonomous Agents
- Establishing goals and decomposing tasks
- Integrating external tools and APIs (search, memory, code execution)
- Coordinating multi-agent systems and implementing human-in-the-loop supervision
Use Cases and Implementation Scenarios
- Contrasting content generation with task orchestration
- Applications in enterprise productivity, customer support, and data extraction
- Ensuring responsible and secure implementation practices
Summary and Next Steps
Requirements
- Foundational knowledge of AI and machine learning principles
- Practical experience with APIs or scripting languages such as Python
- Existing 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 interested in exploring agentic AI systems
Testimonials (1)
the tips and recommended prompts that we can take away from this training