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Course Outline
Introduction to Generative AI and Agentic AI
- Defining Generative AI and Agentic AI.
- Key differences and complementary strengths.
- Industry use cases and current trends.
Generative AI Architecture and Tools
- Transformer models: GPT, LLaMA, Claude, and others.
- Distinctions between fine-tuning and in-context learning.
- Essential 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
- Definition and historical evolution of agentic AI.
- Core architectures: planning, memory, tool use, and self-reflection.
- Leading frameworks: AutoGPT, BabyAGI, CrewAI, LangGraph.
Designing and Deploying Autonomous Agents
- Goal setting and task decomposition strategies.
- Integration of tools and APIs (search, memory, code execution).
- Multi-agent coordination and human-in-the-loop supervision.
Use Cases and Implementation Scenarios
- Comparing 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 concepts.
- Practical experience with APIs or scripting languages such as Python.
- Familiarity with prompt engineering techniques or the usage of large language models.
Audience
- AI developers and engineers.
- Innovation and 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
