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Duration 14 hours
Course Outline
Foundations of Generative and Agentic AI
- Defining Generative AI and Agentic AI
- Distinguishing between the two and understanding their complementary roles
- Industry-specific use cases and emerging trends
Generative AI Architecture and Tooling
- Overview of Transformer models: GPT, LLaMA, Claude, and others
- Fine-tuning strategies versus in-context learning
- Key tools: ChatGPT, Hugging Face Transformers, and Google AI Studio
Prompt Engineering for Precision and Structure
- Developing prompt patterns for writing, coding, and summarization
- Techniques including few-shot, zero-shot, and chain-of-thought prompting
- Leveraging prompt libraries and testing utilities
Insights into Agentic AI
- Evolution and definition of agentic AI concepts
- Core architectural elements: planning, memory, tool usage, and self-reflection
- Leading frameworks: AutoGPT, BabyAGI, CrewAI, and LangGraph
Building and Deploying Autonomous Agents
- Setting goals and breaking down complex tasks
- Seamless integration of tools and APIs for search, memory, and code execution
- Coordinating multi-agent systems and implementing 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 implementation practices
Conclusion and Future Directions
Requirements
- Foundational knowledge of AI and machine learning principles
- Hands-on experience with APIs or scripting languages like Python
- Working familiarity with prompt engineering or the utilization of large language models
Target Audience
- AI developers and software engineers
- Innovation and R&D teams
- Technical product managers investigating agentic AI systems
Testimonials (1)
the tips and recommended prompts that we can take away from this training