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Duration 14 hours
Course Outline
Introduction to Generative AI and Agentic AI
- Defining Generative AI and Agentic AI.
- Understanding their differences and how they synergize.
- Industry applications and emerging trends.
Generative AI Architecture and Tools
- Overview of Transformer models: GPT, LLaMA, Claude, and similar architectures.
- Distinctions between fine-tuning and in-context learning.
- Key tools: ChatGPT, Hugging Face Transformers, and Google AI Studio.
Prompt Engineering for Control and Structure
- Developing prompt patterns for writing, coding, summarization, and more.
- Techniques including few-shot, zero-shot, and chain-of-thought prompting.
- Leveraging prompt libraries and specialized testing tools.
Understanding Agentic AI
- Defining the concept and tracing the evolution of agentic AI.
- Core architectures: planning, memory management, tool use, and self-reflection.
- Leading frameworks: AutoGPT, BabyAGI, CrewAI, and LangGraph.
Designing and Deploying Autonomous Agents
- Strategies for goal setting and task decomposition.
- Integration of external tools and APIs (search, memory, code execution).
- Coordinating multi-agent systems and implementing human-in-the-loop oversight.
Use Cases and Implementation Scenarios
- Contrasting content generation with complex task orchestration.
- Applications in enterprise productivity, customer support, and data extraction.
- Best practices for responsible and secure implementation.
Summary and Next Steps
Requirements
- A solid grasp of fundamental AI and machine learning principles.
- Prior experience interacting with APIs or using scripting languages like Python.
- Basic familiarity with prompt engineering or the utilization of Large Language Models.
Target Audience
- AI developers and engineers.
- Innovation and Research & Development (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
