Generative AI for Beginners

Overview

Generative AI for Beginners is a free, open-source educational curriculum created and maintained by Microsoft. It is designed to introduce students, developers, and AI enthusiasts to the core concepts and practical techniques of generative artificial intelligence. The curriculum takes a structured, project-oriented approach, guiding learners from foundational knowledge of large language models (LLMs) to intermediate and advanced topics such as prompt engineering, retrieval-augmented generation (RAG), fine-tuning, and responsible AI deployment. Every lesson combines concise theoretical exposition with executable code samples, allowing learners to directly observe how generative models behave and how they can be adapted to a variety of tasks.

As a learning resource, the curriculum is built around a sequence of self-contained lessons that incrementally build skills. Early lessons cover the architecture of modern LLMs, including transformer models, tokenization, attention mechanisms, and the concept of context windows. Subsequent lessons dive into practical areas: crafting precise prompts to guide outputs, applying few-shot and chain-of-thought reasoning, using embeddings and vector stores to implement RAG, and fine-tuning smaller open-source models with frameworks such as Hugging Face Transformers. This progression enables learners not only to consume AI content, but to develop the agility needed to select and combine techniques for building real applications, such as Q&A systems, content summarizers, and code generation assistants.

From a technical perspective, the materials emphasize a clear and pragmatic workflow. Learners are encouraged to set up local development environments, run notebooks and command-line scripts, and integrate with cloud services like Azure AI. The course also discusses trade-offs among different model sizes, latency, cost, and performance, and explains how to evaluate generative outputs using both quantitative metrics and qualitative review. Special attention is given to ethical considerations: identifying hallucinations, mitigating harmful bias, ensuring transparency, and respecting privacy and data governance. These topics are treated as an integral component of the learning path, reflecting Microsoft's commitment to responsible artificial intelligence.

Accessible through the public GitHub repository, the curriculum supports self-paced study, making it ideal for independent learners and organized study groups alike. It is also well-suited for being incorporated into university courses and corporate training programs. By the end of the journey, participants will have a solid mental model of generative AI's capabilities and limitations, and they will have built at least one functional prototype. For anyone looking to move from being a passive observer of AI breakthroughs to an active builder, Generative AI for Beginners offers an authoritative and welcoming gateway.

Core capabilities

Free and open source

The complete curriculum is available on GitHub with a permissive license, enabling unrestricted access and reuse.

Structured learning path

Progress logically from fundamental LLM concepts to advanced techniques through a curated sequence of lessons.

Hands-on exercises

Each lesson includes runnable code samples and practical assignments that demonstrate how generative models work in practice.

Prompt engineering guidance

Learn how to craft effective prompts, including few-shot prompting and chain-of-thought reasoning.

RAG implementation

Understand how to build retrieval-augmented generation systems using embeddings and vector stores.

Fine-tuning fundamentals

Gain practical experience adapting open-source models to specialized tasks using tools like Hugging Face.

Responsible AI practices

Address important topics such as hallucination mitigation, bias, transparency, privacy, and safety.

Cloud integration

Learn how to leverage Azure OpenAI and related services for building and deploying generative AI solutions.

Self-paced learning

The material is modular and flexible, supporting individual study as well as group or classroom use.

Use cases

Building a chatbot or virtual assistant from scratch

Implementing a question-answering system over internal documents

Learning how to integrate OpenAI models into existing applications

Developing content summarization or generation tools

Exploring fine-tuning and RAG for domain-specific AI solutions

Preparing for technical interviews or AI-focused certifications

Teaching an introductory generative AI course at a university or bootcamp