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Duration 14 hours
Course Outline
Introduction to Generative AI and Agentic AI
- Defining Generative AI and Agentic AI
- Distinguishing between the two and understanding their complementary nature
- Industry use cases and emerging trends
Generative AI Architecture and Tools
- Transformer models: GPT, LLaMA, Claude, and others
- Comparing fine-tuning with 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
- Few-shot, zero-shot, and chain-of-thought prompting techniques
- Utilizing prompt libraries and testing utilities
Understanding Agentic AI
- Defining agentic AI and tracing its evolution
- Core architectures: planning, memory, tools, and self-reflection
- Leading frameworks: AutoGPT, BabyAGI, CrewAI, LangGraph
Designing and Deploying Autonomous Agents
- Setting goals and decomposing tasks
- Integrating tools and APIs (search, memory, code execution)
- Coordinating multi-agent systems and implementing human-in-the-loop oversight
Use Cases and Implementation Scenarios
- Differentiating content generation from task orchestration
- Applications in enterprise productivity, customer support, and data extraction
- Ensuring responsible and secure implementation practices
Summary and Next Steps
Requirements
- Solid understanding of AI and machine learning concepts
- Hands-on experience with APIs or scripting languages like Python
- 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 investigating agentic AI systems
Testimonials (1)
the tips and recommended prompts that we can take away from this training
