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Course Outline
Introduction to Generative and Agentic AI
- Defining Generative AI and Agentic AI
- Analyzing the differences and synergies between the two
- Industry-specific use cases and emerging trends
Generative AI: Architecture and Tooling
- Transformer models: GPT, LLaMA, Claude, and alternatives
- Distinctions between fine-tuning and in-context learning
- Key tools: ChatGPT, Hugging Face Transformers, 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
Deep Dive into Agentic AI
- Defining the concept and evolution of agentic AI
- Core architectures: planning, memory, tool usage, and self-reflection
- Leading frameworks: AutoGPT, BabyAGI, CrewAI, 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 and implementing human-in-the-loop supervision
Application Scenarios and Use Cases
- Contrasting content generation with task orchestration
- Applications in enterprise productivity, customer support, and data extraction
- Ensuring responsible and secure implementation practices
Summary and Future Directions
Requirements
- Foundational knowledge of AI and machine learning concepts
- Proficiency in working with APIs or scripting languages, particularly Python
- Prior experience with prompt engineering or utilizing Large Language Models
Intended Audience
- AI developers and engineers
- Innovation and R&D teams
- Technical product managers interested in agentic AI systems
14 Hours
Testimonials (1)
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