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Duration 14 hours
Course Outline
Introduction to Generative AI and Agentic AI
- Defining Generative AI and Agentic AI.
- Analyzing their distinctions and synergies.
- Reviewing key use cases and industry trends.
Architecture and Tooling for Generative AI
- Overview of Transformer models: GPT, LLaMA, Claude, and others.
- Distinguishing between fine-tuning and in-context learning.
- Key tools: ChatGPT, Hugging Face Transformers, and Google AI Studio.
Prompt Engineering for Enhanced Control and Structure
- Developing prompt patterns for writing, coding, summarization, and more.
- Techniques including few-shot, zero-shot, and chain-of-thought prompting.
- Leveraging prompt libraries and testing utilities.
Deep Dive into Agentic AI
- Defining the scope and evolution of agentic AI.
- Core architectures: planning, memory, tool usage, and self-reflection.
- Leading frameworks: AutoGPT, BabyAGI, CrewAI, and LangGraph.
Building and Launching Autonomous Agents
- Establishing goals and decomposing complex tasks.
- Connecting external tools and APIs (search, memory, code execution).
- Coordinating multi-agent systems with human-in-the-loop oversight.
Practical Applications and Implementation Scenarios
- Contrasting content generation with task orchestration.
- Applications in enterprise productivity, customer support, and data extraction.
- Ensuring responsible and secure implementation practices.
Key Takeaways and Future Directions
Requirements
- A solid grasp of fundamental AI and machine learning principles.
- Practical experience interacting with APIs or scripting languages like Python.
- Prior exposure to prompt engineering or the utilization of large language models.
Target Audience
- AI developers and software engineers.
- Innovation and R&D teams seeking advanced AI capabilities.
- Technical product managers interested in exploring agentic AI systems.
Testimonials (1)
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