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Course Outline
Overview of Generative AI and Agentic AI
- Defining Generative AI and Agentic AI.
- Analysing the differences and synergies between the two.
- Examining industry-specific use cases and emerging trends.
Generative AI Architecture and Ecosystem
- Transformer models: GPT, LLaMA, Claude, and other key architectures.
- Differentiating between fine-tuning and in-context learning.
- Key tools: ChatGPT, Hugging Face Transformers, and Google AI Studio.
Prompt Engineering for Precision and Structure
- Developing prompt patterns for writing, coding, and summarisation.
- Leveraging few-shot, zero-shot, and chain-of-thought prompting strategies.
- Utilising prompt libraries and testing frameworks.
The Landscape of Agentic AI
- Understanding the definition and evolutionary trajectory of agentic AI.
- Core architectural components: planning, memory, tool usage, and self-reflection.
- Leading frameworks: AutoGPT, BabyAGI, CrewAI, and LangGraph.
Architecting and Deploying Autonomous Agents
- Strategies for goal setting and effective task decomposition.
- Integrating external tools and APIs (including search, memory, and code execution).
- Coordinating multi-agent systems and implementing human-in-the-loop supervision.
Real-World Applications and Implementation
- Distinguishing between content generation and task orchestration.
- Applications in enterprise productivity, customer support, and data extraction.
- Ensuring responsible and secure implementation practices.
Summary and Future Directions
Requirements
- A solid foundation in AI and machine learning principles.
- Proficiency with APIs or scripting languages, particularly Python.
- Prior experience with prompt engineering or the utilisation of Large Language Models.
Target Audience
- AI developers and engineers.
- Innovation and Research and Development (R&D) teams.
- Technical product managers seeking to explore agentic AI systems.
14 Hours
Testimonials (1)
the tips and recommended prompts that we can take away from this training
