Generative AI for Beginners

Teaches generative AI app development through 21 lessons covering LLM basics, prompting, chat, search, image generation, agents, RAG, fine-tuning, small models, and responsible AI.

Visit Website

Introduction

Many beginner resources stop at concepts or isolated API calls. This course walks from foundations into app patterns such as chat, search, images, agents, RAG, lifecycle, security, and fine-tuning.

What Sets It Apart

Lessons mix conceptual explanations with Python and TypeScript examples, helping learners connect model behavior to application structure. The sequence mirrors how builders mature from prompts to retrieval, agents, UX, and operational concerns.

Who Should Use It

Great fit for developers who know programming and need a guided path into generative AI apps. Look elsewhere for research-level model internals, production evaluation systems, or advanced infrastructure tuning.

Back

Information

Websitegithub.com
OrganizationsMicrosoft
AuthorsMicrosoft Cloud Advocates
Published date2023/06/01

More Items

Large Language Model Tutorials2025

All-in-RAG

dalvqwDatawhale (datawhalechina)

Practical, full-stack tutorial for building Retrieval-Augmented Generation (RAG) systems—covers data preprocessing, vector embedding and indexing, hybrid and multimodal retrieval, generation integration, evaluation and production-ready engineering. Includes hands-on projects and examples for developers with Python experience.

RAG embeddings multimodal python llm+4

Machine Learning Foundation Tutorials2021

Artificial Intelligence for Beginners - A Curriculum

Dmitry Soshnikov, Jen Looper +3Microsoft

A 12-week, 24-lesson beginner-friendly AI curriculum with executable Jupyter notebooks, quizzes and labs that teach neural networks, computer vision, NLP, generative models and ethics using PyTorch and TensorFlow examples.

course pytorch python nlp vision+5

Machine Learning Foundation Tutorials2026

Zen and the Art of AI Research

Jack Morris

Frames AI research as a trainable practice of reading, building, debugging, and fast feedback. The essay is most useful for researchers learning how to avoid hype-chasing, benchmark tunnel vision, and agent-induced blind spots.

ai blog tutorial foundation LLM+2