Llama 4: Ai Mastering Prompt Engineering
Published 6/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 17m | Size: 1.1 GB
Build, optimize, and deploy Llama 4 with prompt engineering techniques using Google Colab and Hugging Face
What you'll learn
How Llama 4 works under the hood: architecture, tokenization, and attention
How to set up a working Llama 4 environment using Google Colab and Hugging Face
How to write powerful prompts-from zero-shot to few-shot examples
Techniques to control tone, style, and response length in AI outputs
How to troubleshoot prompt errors, repetition, and hallucinations
How to compare Llama 4 with GPT-4, Claude, and other leading LLMs
How to stay up to date with evolving LLM tools, communities, and research sources
Requirements
For this course you need Basic familiarity with Python (no advanced coding needed)
A free Google account to use Google Colab
No prior experience with Hugging Face or Llama 4 required-everything is taught step-by-step
Description
In today's Generative AI-driven world, staying competitive, creative, and efficient requires more than surface-level tools-it demands a deep understanding of the models shaping our future. This course is built for developers, researchers, educators, and AI enthusiasts who want to master Meta's Llama 4 model and gain real skills in prompt engineering and inference.By integrating Llama 4 into your Generative AI workflow, you'll unlock the ability to run open-weight models in Google Colab, generate high-quality outputs, and apply prompt strategies that maximize accuracy, relevance, and tone-whether you're writing, coding, or analyzing.This course is designed for both theory and real-world application, delivering value through:Bite-sized lessons optimized for clear learningStep-by-step walkthroughs of setup, prompting, and output analysisLifetime access with updates for model versions and toolingActionable examples and guidance for using Generative AI models responsibly and effectivelyA foundation in IT Fundamentals to support hands-on implementation and understandingWhat You'll Learn in This Course:Set up and run Llama 4 using Hugging Face and Google ColabUnderstand how tokenization, attention, and model architecture shape Generative AI outputsWrite effective zero-shot and few-shot prompts across use casesControl tone, style, and length of LLM responses with precisionTroubleshoot misleading or failed outputs with prompt refinementCompare Llama 4 with other Generative AI models like GPT-4, Claude, and MistralApply ethical practices to avoid bias and manage hallucinationsUse temperature, top-k, and top-p to guide creativity and accuracyStay current with new model research, tools, and communitiesStrengthen your command of IT Fundamentals through practical, AI-related examplesWho This Course Is For
evelopers and engineers working with open-source LLMsAI enthusiasts, students, and researchers building foundational knowledgeMarketers and content creators who want smarter control over outputsBusiness professionals exploring practical AI integrationAnyone interested in understanding Generative AI models and reinforcing their IT FundamentalsWhat's Included:Colab-ready templates with reusable Python codeReal-world examples: summarization, content generation, codingPrompt templates for marketing, education, and product use casesSide-by-side comparisons of Llama 4 and proprietary LLMsCertificate of completion to showcase your LLM expertiseGet Started Now!Mastering Llama 4 isn't just about copying code-it's about understanding why it works and how to use it responsibly. This course gives you the theoretical foundation, technical tools, and hands-on practice to work with Llama 4 effectively and ethically.Enroll today to sharpen your Generative AI skills, reinforce your IT Fundamentals, build smarter language model applications, and take your LLM projects from idea to execution.Let's learn more about Llama 4-join the course now!Who this course is for
AI enthusiasts and tech learners
Machine learning engineers and data scientists
Developers building LLM-powered apps
Educators creating AI-driven content
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