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Duration 14 hours
Course Outline
Introduction to Generative and Agentic AI
- Defining Generative AI and Agentic AI
- Understanding their differences and complementary roles
- Key use cases and emerging trends across sectors
Generative AI: Architecture and Tooling
- Transformer models including GPT, LLaMA, Claude, and others
- Fine-tuning strategies versus in-context learning
- Essential tools: ChatGPT, Hugging Face Transformers, and Google AI Studio
Prompt Engineering for Precision and Structure
- Effective prompt patterns for writing, coding, and summarization
- Techniques: Few-shot, zero-shot, and chain-of-thought prompting
- Leveraging prompt libraries and testing utilities
Decoding Agentic AI
- The definition and evolution of agentic AI
- Core architectures: planning, memory, tool usage, and self-reflection
- Leading frameworks: AutoGPT, BabyAGI, CrewAI, and LangGraph
Building and Deploying Autonomous Agents
- Setting goals and breaking down tasks effectively
- Connecting tools and APIs (search, memory, and code execution)
- Orchestrating multi-agent systems with human-in-the-loop oversight
Real-World Applications and Scenarios
- Contrasting content generation with task orchestration
- Enhancing enterprise productivity, customer support, and data extraction
- Ensuring responsible and secure implementation practices
Wrap-up and Future Directions
Requirements
- Foundational knowledge of AI and machine learning principles
- Hands-on experience with APIs or scripting languages like Python
- Prior exposure to prompt engineering or large language model (LLM) utilization
Target Audience
- AI developers and software 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
