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Duration 14 hours
Course Outline
Introduction to Generative AI and Agentic AI
- Defining Generative AI and Agentic AI
- Understanding the distinctions and synergies between the two
- Key use cases and industry trends
Generative AI Architecture and Tooling
- Transformer models: including GPT, LLaMA, Claude, and others
- Distinguishing fine-tuning from in-context learning
- Key tools: ChatGPT, Hugging Face Transformers, and Google AI Studio
Prompt Engineering for Control and Structure
- Prompt patterns for writing, coding, summarisation, and more
- Techniques for few-shot, zero-shot, and chain-of-thought prompting
- Leveraging prompt libraries and testing utilities
Understanding Agentic AI
- The definition and evolution of agentic AI
- Core architectures: planning, memory, tools, and self-reflection
- Popular frameworks: AutoGPT, BabyAGI, CrewAI, and LangGraph
Designing and Deploying Autonomous Agents
- Setting goals and decomposing tasks
- Integrating tools and APIs for search, memory, and code execution
- Coordinating multi-agent systems and implementing 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
- Ensuring responsible and secure implementation practices
Summary and Future Directions
Requirements
- A solid understanding of AI and machine learning principles
- Practical experience with APIs or scripting languages such as Python
- Familiarity with prompt engineering or the utilisation 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
