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Course Outline
Introduction to Generative and Agentic AI
- Defining Generative AI and Agentic AI.
- Analyzing the differences and complementary nature of these technologies.
- Reviewing key use cases and industry trends.
Generative AI Architecture and Toolset
- Examination of Transformer models, including GPT, LLaMA, Claude, and others.
- Comparing fine-tuning techniques with in-context learning.
- Overview of key tools such as ChatGPT, Hugging Face Transformers, and Google AI Studio.
Prompt Engineering for Control and Structure
- Developing prompt patterns for writing, coding, summarization, and more.
- Implementing few-shot, zero-shot, and chain-of-thought prompting strategies.
- Leveraging prompt libraries and testing utilities.
Understanding Agentic AI
- Exploring the definition and evolution of agentic AI.
- Studying core architectures: planning, memory, tool usage, and self-reflection.
- Surveying popular frameworks like AutoGPT, BabyAGI, CrewAI, and LangGraph.
Designing and Deploying Autonomous Agents
- Techniques for goal setting and task decomposition.
- Integration of external tools and APIs (including search, memory, and code execution).
- Strategies for multi-agent coordination and human-in-the-loop oversight.
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 Future Directions
Requirements
- Foundational knowledge of AI and machine learning principles.
- Practical experience with APIs or scripting languages, particularly Python.
- Prior exposure to prompt engineering or the utilization of large language models.
Target Audience
- AI developers and software engineers.
- Innovation and R&D teams.
- Technical product managers interested in agentic AI systems.
14 Hours
Testimonials (1)
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