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Course Outline
Foundations of Generative and Agentic AI
- Defining Generative AI and Agentic AI
- Distinguishing between the two and understanding their synergies
- Industry-specific use cases and emerging trends
Architecture and Tooling for Generative AI
- Transformer models: GPT, LLaMA, Claude, and other variants
- Fine-tuning strategies versus in-context learning approaches
- Key tools: ChatGPT, Hugging Face Transformers, and Google AI Studio
Prompt Engineering for Precision and Structure
- Developing prompt patterns for writing, coding, summarization, and more
- Applying few-shot, zero-shot, and chain-of-thought prompting techniques
- Utilizing prompt libraries and testing utilities
Deep Dive into Agentic AI
- Origins and evolution of agentic AI concepts
- Core architectures: planning, memory, tool usage, and self-reflection
- Leading frameworks: AutoGPT, BabyAGI, CrewAI, and LangGraph
Building and Deploying Autonomous Agents
- Establishing goals and breaking down complex tasks
- Connecting tools and APIs for search, memory, and code execution
- Coordinating multi-agent systems and incorporating human oversight
Practical Applications and Scenarios
- Contrasting content generation with task orchestration
- Enhancing enterprise productivity, customer support, and data extraction
- Ensuring secure and responsible implementation strategies
Recap and Future Directions
Requirements
- A foundational grasp of AI and machine learning principles
- Practical experience with APIs or scripting languages like Python
- Familiarity with prompt engineering or the application of Large Language Models
Intended Audience
- AI developers and engineers
- Innovation and R&D teams
- Technical product managers investigating agentic AI architectures
14 Hours
Testimonials (1)
the tips and recommended prompts that we can take away from this training
