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Duration 14 hours
Course Outline
Introduction to Generative AI 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 Tools
- Transformer models: GPT, LLaMA, Claude, and similar architectures.
- Contrasting fine-tuning with in-context learning.
- Key tools: ChatGPT, Hugging Face Transformers, and Google AI Studio.
Prompt Engineering for Control and Structure
- 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.
Understanding Agentic AI
- The definition and evolutionary trajectory of agentic AI.
- Core architectures: planning, memory, tools, and self-reflection.
- Leading frameworks: AutoGPT, BabyAGI, CrewAI, and LangGraph.
Designing and Deploying Autonomous Agents
- Establishing goals and decomposing tasks.
- Integrating tools and APIs for search, memory, and code execution.
- Coordinating multi-agent systems with human-in-the-loop oversight.
Use Cases and Implementation Scenarios
- Differentiating between content generation and task orchestration.
- Applications in enterprise productivity, customer support, and data extraction.
- Ensuring responsible and secure implementation practices.
Summary and Next Steps
Requirements
- A solid grasp of AI and machine learning principles.
- Proficiency in working with APIs or scripting languages like Python.
- Prior exposure to prompt engineering or large language model applications.
Target Audience
- AI developers and engineers.
- Innovation and R&D teams.
- Technical product managers exploring agentic AI ecosystems.
Testimonials (1)
the tips and recommended prompts that we can take away from this training
