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Duration 14 hours
Course Outline
Fundamentals of Generative and Agentic AI
- Defining Generative AI and Agentic AI
- Distinguishing between the two and identifying their synergies
- Industry-specific applications and emerging trends
Generative AI Architecture and Ecosystem
- Transformer-based models: GPT, LLaMA, Claude, and alternatives
- Fine-tuning strategies versus in-context learning
- Key tools: ChatGPT, Hugging Face Transformers, Google AI Studio
Prompt Engineering for Precision and Structure
- Effective prompt patterns for writing, coding, summarization, and more
- Techniques including few-shot, zero-shot, and chain-of-thought prompting
- Leveraging prompt libraries and validation tools
The Landscape of Agentic AI
- Origins and evolution of agentic systems
- Core architectural components: planning, memory, tool usage, and self-reflection
- Leading frameworks: AutoGPT, BabyAGI, CrewAI, LangGraph
Building and Launching Autonomous Agents
- Establishing goals and breaking down complex tasks
- Connecting external tools and APIs for search, memory, and code execution
- Coordinating multi-agent systems and implementing human-in-the-loop oversight
Practical Applications and Scenarios
- Differentiating between content generation and task orchestration
- Enhancing enterprise efficiency, customer support, and data extraction processes
- Ensuring secure and responsible implementation
Recap and Future Directions
Requirements
- Fundamental comprehension of AI and machine learning principles
- Hands-on experience with API development or scripting languages like Python
- Working knowledge of prompt engineering or the utilization of Large Language Models
Target Audience
- AI developers and software engineers
- Teams focused on innovation and Research & Development
- Technical product managers investigating agentic AI architectures
Testimonials (1)
the tips and recommended prompts that we can take away from this training
