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Duration 14 hours
Course Outline
Foundations of Generative and Agentic AI
- Defining Generative AI and Agentic AI
- Analyzing the differences and synergies between the two paradigms
- Industry-specific use cases and emerging trends
Architecture and Ecosystem of Generative AI
- Exploring transformer models, including GPT, LLaMA, and Claude
- Contrasting fine-tuning with in-context learning methods
- Key platforms: ChatGPT, Hugging Face Transformers, and Google AI Studio
Mastering Prompt Engineering for Precision
- Developing prompt patterns for writing, coding, and summarization
- Techniques involving few-shot, zero-shot, and chain-of-thought reasoning
- Leveraging prompt libraries and validation tools
Deep Dive into Agentic AI
- The evolution and definition of agentic frameworks
- Core components: planning mechanisms, memory structures, tool usage, and self-reflection
- Leading frameworks such as AutoGPT, BabyAGI, CrewAI, and LangGraph
Building and Launching Autonomous Agents
- Strategies for goal definition and task breakdown
- Connecting tools and APIs for search, memory management, and code execution
- Coordinating multi-agent systems and implementing human oversight
Real-World Applications and Implementation
- Distinguishing between content generation and complex task orchestration
- Applications in enterprise productivity, customer service, and data processing
- Ensuring secure and responsible deployment practices
Conclusions and Future Directions
Requirements
- Foundational knowledge of AI and machine learning principles
- Hands-on experience with APIs or scripting languages like Python
- Basic familiarity with prompt engineering or interacting with large language models
Target Audience
- AI developers and software engineers
- Innovation and R&D departments
- Technical product managers seeking to implement agentic AI systems
Testimonials (1)
the tips and recommended prompts that we can take away from this training