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Duration 14 hours
Course Outline
Introduction to Generative AI and Agentic AI
- Defining Generative AI and Agentic AI.
- Examining the differences and synergies between the two.
- Industry trends and diverse use cases.
Generative AI Architecture and Tools
- Transformer models: GPT, LLaMA, Claude, and other key architectures.
- Distinguishing between fine-tuning and in-context learning.
- Key tools: ChatGPT, Hugging Face Transformers, and Google AI Studio.
Prompt Engineering for Control and Structure
- Effective prompt patterns for writing, coding, and summarization.
- Techniques including few-shot, zero-shot, and chain-of-thought prompting.
- Utilizing prompt libraries and testing frameworks.
Understanding Agentic AI
- The definition and evolutionary path of agentic AI.
- Core architectures: planning, memory, tool usage, and self-reflection.
- Leading frameworks: AutoGPT, BabyAGI, CrewAI, and LangGraph.
Designing and Deploying Autonomous Agents
- Strategies for goal setting and task decomposition.
- Integrating external tools and APIs (search, memory, code execution).
- Multi-agent coordination 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 Next Steps
Requirements
- A foundational understanding of AI and machine learning principles.
- Professional experience with API development or scripting languages like Python.
- Prior exposure to prompt engineering or the usage of Large Language Models.
Target Audience
- AI developers and software engineers.
- Innovation and R&D teams.
- Technical product managers looking to explore agentic AI ecosystems.
Testimonials (1)
the tips and recommended prompts that we can take away from this training