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Duration 14 hours
Course Outline
Overview of Generative and Agentic AI
- Defining Generative AI and Agentic AI
- Distinctions and synergies between the two approaches
- Industry-specific use cases and emerging trends
Generative AI Architecture and Tooling
- Transformer models: GPT, LLaMA, Claude, and similar architectures
- Comparing fine-tuning with in-context learning methods
- Essential tools: ChatGPT, Hugging Face Transformers, and Google AI Studio
Prompt Engineering for Precision and Structure
- Developing prompt patterns for writing, coding, and summarization
- Techniques for few-shot, zero-shot, and chain-of-thought prompting
- Leveraging prompt libraries and testing utilities
The Fundamentals of Agentic AI
- Defining the scope and evolution of agentic AI
- Core architectures: planning, memory, tool use, and self-reflection
- Leading frameworks: AutoGPT, BabyAGI, CrewAI, and LangGraph
Building and Deploying Autonomous Agents
- Strategies for goal setting and task decomposition
- Integrating external tools and APIs (search, memory, code execution)
- Coordinating multi-agent systems with human-in-the-loop oversight
Practical 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
Conclusions and Path Forward
Requirements
- Foundational knowledge of AI and machine learning principles
- Hands-on experience with APIs or scripting languages like Python
- Familiarity with prompt engineering or the usage of large language models
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
