Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 14 hours
Course Outline
Introduction to Generative AI and Agentic AI
- Defining the concepts: What constitutes Generative AI and what is Agentic AI?
- Analyzing the distinctions between the two and how they synergize
- Reviewing key use cases and emerging trends across various sectors
Architecture and Tools for Generative AI
- Examination of Transformer models, including GPT, LLaMA, Claude, and other variants
- Differentiating between fine-tuning approaches and in-context learning
- Utilizing leading tools such as ChatGPT, Hugging Face Transformers, and Google AI Studio
Prompt Engineering for Control and Structure
- Developing prompt patterns for tasks like writing, coding, and summarization
- Implementing few-shot, zero-shot, and chain-of-thought prompting techniques
- Leveraging prompt libraries and testing utilities to refine outputs
Understanding Agentic AI
- Defining the scope and evolution of agentic AI systems
- Exploring core architectural components: planning, memory management, tool use, and self-reflection
- Reviewing prominent frameworks such as AutoGPT, BabyAGI, CrewAI, and LangGraph
Designing and Deploying Autonomous Agents
- Establishing objectives and breaking down complex tasks
- Incorporating external tools and APIs for search, memory, and code execution
- Managing multi-agent coordination and implementing human-in-the-loop oversight
Use Cases and Implementation Scenarios
- Contrasting content generation workflows with task orchestration processes
- Applying AI to enterprise productivity, customer support, and data extraction
- Ensuring secure and responsible implementation strategies
Summary and Next Steps
Requirements
- A solid foundation in AI and machine learning principles
- Proficiency in utilizing APIs or scripting languages, particularly Python
- Prior exposure to prompt engineering techniques or the operation of Large Language Models
Target Audience
- AI developers and software engineers
- Innovation and Research & Development (R&D) teams
- Technical product managers investigating the integration of agentic AI systems
Testimonials (1)
the tips and recommended prompts that we can take away from this training
