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Course Outline
Introduction to Generative and Agentic AI
- Defining Generative AI and Agentic AI.
- Key differences and complementary aspects.
- Industry use cases and current trends.
Generative AI Architecture and Tools
- Transformer models including GPT, LLaMA, Claude, and others.
- Distinguishing between fine-tuning and in-context learning.
- Key tools: ChatGPT, Hugging Face Transformers, Google AI Studio.
Prompt Engineering for 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 tools.
Understanding Agentic AI
- The definition and evolution of agentic AI.
- Core architectures: planning, memory, tool usage, and self-reflection.
- Prominent frameworks: AutoGPT, BabyAGI, CrewAI, LangGraph.
Designing and Deploying Autonomous Agents
- Goal setting and task decomposition strategies.
- Integrating tools and APIs for search, memory, and code execution.
- Multi-agent coordination and 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.
- Principles of responsible and secure implementation.
Summary and Next Steps
Requirements
- A foundational understanding of AI and machine learning concepts.
- Hands-on experience with APIs or scripting languages, particularly Python.
- Familiarity with prompt engineering or the usage of large language models.
Target Audience
- AI developers and engineers.
- Innovation and Research & Development (R&D) teams.
- Technical product managers investigating agentic AI systems.
14 Hours
Testimonials (1)
the tips and recommended prompts that we can take away from this training
