Llama 4: AI Mastering Prompt Engineering

Course Title: Llama 4: AI Mastering Prompt Engineering
Instructor: Anton Voroniuk
Target Audience:
- Developers and engineers working with open-source LLMs
- AI enthusiasts, students, and researchers building foundational knowledge
- Marketers and content creators who want smarter control over outputs
- Business professionals exploring practical AI integration
- Anyone interested in understanding Generative AI models and reinforcing their IT Fundamentals
Prerequisites:
- Basic familiarity with Python (no advanced coding needed)
- A free Google account to use Google Colab

Key Topics Covered:
- How Llama 4 works under the hood: architecture, tokenization, and attention
- Setting up a working Llama 4 environment using Google Colab and Hugging Face
- Writing powerful prompts—from zero-shot to few-shot examples
- Techniques to control tone, style, and response length in AI outputs
- Troubleshooting prompt errors, repetition, and hallucinations
- Comparing Llama 4 with GPT-4, Claude, and other leading LLMs
- Staying up to date with evolving LLM tools, communities, and research sources
Key Skills Learned:
- Set up and run Llama 4 using Hugging Face and Google Colab
- Understand how tokenization, attention, and model architecture shape Generative AI outputs
- Write effective zero-shot and few-shot prompts across use cases
- Control tone, style, and length of LLM responses with precision
- Troubleshoot misleading or failed outputs with prompt refinement
- Compare Llama 4 with other Generative AI models like GPT-4, Claude, and Mistral
- Apply ethical practices to avoid bias and manage hallucinations
- Use temperature, top-k, and top-p to guide creativity and accuracy
- Stay current with new model research, tools, and communities
- Strengthen command of IT Fundamentals through practical, AI-related examples

Duration: 1.5 hours on-demand video
Format: Self-paced online course
Resources:
- 1 downloadable resource
- Access on mobile and TV
- Certificate of completion