Generative AI for Beginners
by John Aziz & Team · Microsoft
Our Verdict
Worth takingMicrosoft's Generative AI for Beginners is a free, open-source GitHub curriculum from Microsoft Cloud Advocates that is one of the strongest no-cost on-ramps into building with LLMs, and it is worth taking if you want a structured, code-first tour of the modern GenAI stack. Note the catalog lists '18 lessons' but the live repository now contains 21 lessons spanning intro to LLMs, prompt engineering, RAG and vector databases, function calling, AI agents, fine-tuning, and building with open-source, Mistral, and Meta models. Its popularity is real and verifiable: roughly 112,000 GitHub stars and 60,000+ forks, plus 50+ community translations. The catch is that it is primarily text and Jupyter-notebook based with only a partial companion video series, it grants no certificate, and running the 'Build' lessons requires you to bring your own model access (GitHub Models free tier, Azure OpenAI, or an OpenAI API key), which adds setup and potential cost. Treat the 4.6 catalog rating as unverified, since this is a GitHub repo rather than a rated platform course; the honest popularity signal is the star count, not a star rating.
It is a free, frequently updated, broad, and genuinely hands-on introduction to building generative AI applications from a credible source (Microsoft), with a clear Learn-then-Build structure across 21 lessons. The main trade-offs (text-heavy delivery, no certificate, and bring-your-own API key setup) are acceptable for self-motivated learners, so for the right audience it is an easy recommendation.
Best for: Developers and technically curious beginners who already have at least basic Python or TypeScript familiarity and want a free, structured, project-oriented path to actually building LLM apps (chatbots, RAG search, image generation, function calling, agents) rather than just watching theory. It is ideal for self-directed learners comfortable reading docs, working in notebooks, and setting up their own model API access.
Skip if: Complete non-programmers wanting a passive, video-led experience; people who need an accredited certificate for their resume (none is offered); learners who want a deep mathematical or research-level treatment of how models work; and anyone unwilling or unable to configure an Azure OpenAI / GitHub Models / OpenAI API key to run the code exercises.
About This Course
18-lesson GitHub curriculum covering LLMs, prompt engineering, RAG, AI agents, and building generative AI applications.
What You'll Learn
Curriculum
Configuring the development environment and required model access.
Foundational concepts of generative AI and how large language models work (Learn).
Choosing the right model for a specific use case (Learn).
Ethical and responsible AI development practices (Learn).
Core prompt construction and optimization techniques (Build).
Advanced prompting strategies and best practices (Build).
Developing text-generation apps with Python/TypeScript samples (Build).
Designing and integrating chat applications (Build).
Embedding-based semantic search implementation (Build).
Creating image-generation apps (Build).
Using low-code tools to build AI solutions (Build).
Function calling to connect models to external systems (Build).
UX principles specific to AI-powered products (Learn).
AI security threats and mitigation strategies (Learn).
LLM lifecycle management and LLMOps tooling (Learn).
RAG architecture and vector database integration (Build).
Working with open-source models via Hugging Face (Build).
Building AI agents and agent frameworks (Build).
Methodology for fine-tuning large language models (Build).
Advantages of and building with small language models (Learn).
Features and capabilities of Mistral models (Learn).
Characteristics of and building with Meta (Llama) models (Learn).
Prerequisites
- Basic knowledge of Python or TypeScript is helpful (the repo links absolute-beginner Python/TS courses if needed)
- A GitHub account to fork the repository and, for many lessons, to use the GitHub Models free tier
- Access to a model provider to run Build lessons: GitHub Marketplace Models, Azure OpenAI Service, or an OpenAI API key
- Comfort working in Jupyter notebooks and a local or cloud (Codespaces) dev environment
Instructor
John Aziz & Team
Instructor · Microsoft
Pros & Cons
Pros
- Completely free and open-source (MIT) from a credible maintainer (Microsoft Cloud Advocates), with no paywall and no upsell to complete the material
- Broad, current coverage of the real GenAI stack: prompt engineering, RAG, vector DBs, function calling, agents, fine-tuning, and open-source/Mistral/Meta models
- Genuinely hands-on Learn-then-Build structure with runnable code samples in both Python and TypeScript
- Massive validation and active maintenance: ~112K GitHub stars, 60K+ forks, 50+ community translations, and a companion video series plus Azure AI Foundry Discord for support
- Modular and self-paced, so you can start at any lesson and skip topics you already know
Cons
- Primarily text and Jupyter-notebook based; the companion video series is only partial (some later lessons marked 'Video Coming Soon'), so it is not a polished video course
- No certificate of completion, which matters if you need credentials for a resume
- Build lessons require you to bring your own model access (Azure OpenAI / GitHub Models / OpenAI API), adding setup friction and potential API costs beyond the free tiers
- Breadth over depth: as a beginner survey it skims many advanced topics (fine-tuning, agents) rather than teaching any one to production depth, and assumes some prior coding ability despite the 'beginner' label
Alternatives To Consider
Frequently Asked Questions
Is Generative AI for Beginners free?
Yes — Generative AI for Beginners is free to access. The course content is 100% free (open-source, MIT licensed) and no certificate is sold. The only potential cost is model API usage when running the Build lessons: the GitHub Marketplace Models free tier covers most exercises at no cost, while heavier use of Azure OpenAI or the OpenAI API is pay-as-you-go. Microsoft for Startups Azure credits are referenced for those who qualify.
Who is Generative AI for Beginners for?
Developers and technically curious beginners who already have at least basic Python or TypeScript familiarity and want a free, structured, project-oriented path to actually building LLM apps (chatbots, RAG search, image generation, function calling, agents) rather than just watching theory. It is ideal for self-directed learners comfortable reading docs, working in notebooks, and setting up their own model API access.
What will you learn in Generative AI for Beginners?
What generative AI and large language models are, and how to explore and compare different models for a given use case; Prompt engineering from fundamentals through advanced techniques to improve and constrain model outputs; Building text-generation and chat applications with code samples in both Python and TypeScript; Retrieval-Augmented Generation (RAG) and embedding-based search using vector databases.
What are the prerequisites for Generative AI for Beginners?
Basic knowledge of Python or TypeScript is helpful (the repo links absolute-beginner Python/TS courses if needed); A GitHub account to fork the repository and, for many lessons, to use the GitHub Models free tier; Access to a model provider to run Build lessons: GitHub Marketplace Models, Azure OpenAI Service, or an OpenAI API key; Comfort working in Jupyter notebooks and a local or cloud (Codespaces) dev environment.
Is Generative AI for Beginners worth it?
It is a free, frequently updated, broad, and genuinely hands-on introduction to building generative AI applications from a credible source (Microsoft), with a clear Learn-then-Build structure across 21 lessons. The main trade-offs (text-heavy delivery, no certificate, and bring-your-own API key setup) are acceptable for self-motivated learners, so for the right audience it is an easy recommendation.
How we reviewed this course
This is an independent editorial assessment by Cursarium, based on Microsoft's published course materials and aggregated public learner feedback (last reviewed 2026-06). We have not independently completed the course. Links to providers are standard references, not paid placements.
Sources
- Official course repository (syllabus, 21 lessons, prerequisites, star/fork counts, Learn/Build structure, model-access options) - GitHub
- Official course README (full lesson list, Python/TypeScript samples, translations, video links)
- Companion video series on Microsoft Learn (Generative AI for Beginners show)
- Third-party walkthrough/review of the 21-lesson repo (beginner-to-advanced framing) - Medium, Let's Code Future