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Course Outline
Intro to Generative AI and Agentic AI
- Defining Generative AI and Agentic AI.
- Analyzing the distinctions and synergies between the two paradigms.
- Reviewing key use cases and industry trends.
Generative AI Architecture and Tooling
- Transformer models, including GPT, LLaMA, Claude, and others.
- Differentiating between fine-tuning and in-context learning.
- Essential tools: ChatGPT, Hugging Face Transformers, Google AI Studio.
Prompt Engineering for Control and Structure
- Applying prompt patterns for writing, coding, summarization, and more.
- Utilizing few-shot, zero-shot, and chain-of-thought prompting techniques.
- Working with prompt libraries and testing utilities.
Exploring Agentic AI
- Tracing the definition and evolution of agentic AI.
- Core architectures: planning, memory, tools, and self-reflection.
- Leading frameworks: AutoGPT, BabyAGI, CrewAI, LangGraph.
Creating and Deploying Autonomous Agents
- Establishing goals and breaking down complex tasks.
- Integrating tools and APIs for search, memory, and code execution.
- Coordinating multi-agent systems with human-in-the-loop oversight.
Practical Use Cases and Implementation
- Contrasting content generation with task orchestration.
- Applications in enterprise productivity, customer support, and data extraction.
- Ensuring secure and responsible deployment practices.
Recap and Future Directions
Requirements
- Solid comprehension of fundamental AI and machine learning principles.
- Practical experience with APIs or scripting languages, particularly Python.
- Proficiency in prompt engineering or the utilization of large language models.
Target Audience
- AI developers and engineers.
- Innovation and R&D teams.
- Technical product managers investigating agentic AI ecosystems.
14 Hours
Testimonials (1)
the tips and recommended prompts that we can take away from this training
