# How I learn about generative AI

DevFeed: [How I learn about generative AI](<https://devfeed.tech/articles/how-i-learn-about-generative-ai-21740.md>)

Original publisher: [Read original article](<http://blog.pamelafox.org/2025/08/how-i-learn-about-generative-ai.html>)

Author: Pamela Fox (noreply@blogger.com)

Published: 2025-08-19T05:59:00Z

Content type: opinion

Language: en

Sources: [Pamela Fox](<https://devfeed.tech/sources/pamela-fox.md>)

Topics: [Generative AI](<https://devfeed.tech/topics/generative-ai.md>), [AI Engineering](<https://devfeed.tech/topics/ai-engineering.md>), [LLM evaluation / benchmarking](<https://devfeed.tech/topics/llm-evaluation-benchmarking.md>), [web applications](<https://devfeed.tech/topics/web-applications.md>), [LLMs](<https://devfeed.tech/topics/llms.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>), [Python](<https://devfeed.tech/topics/python.md>), [PyTorch](<https://devfeed.tech/topics/pytorch.md>), [AI-assisted coding](<https://devfeed.tech/topics/ai-assisted-coding.md>), [Transformer](<https://devfeed.tech/topics/transformer.md>), [Deep learning](<https://devfeed.tech/topics/deep-learning.md>), [Language models](<https://devfeed.tech/topics/language-models.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-assisted-coding](<https://devfeed.tech/tags/ai-assisted-coding.md>), [ai-engineering](<https://devfeed.tech/tags/ai-engineering.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [llm](<https://devfeed.tech/tags/llm.md>), [openai](<https://devfeed.tech/tags/openai.md>), [python](<https://devfeed.tech/tags/python.md>), [pytorch](<https://devfeed.tech/tags/pytorch.md>), [transformer-architecture](<https://devfeed.tech/tags/transformer-architecture.md>)

## AI overview

The author shares the books, videos, newsletters, communities, and blogs they used to learn generative AI. The resources cover AI engineering, building large language models with Python and PyTorch, neural networks, model evaluation, retrieval-augmented generation, and AI-assisted coding.

## Source excerpt

I do not consider myself an expert in generative AI, but I now know enough to build full-stack web applications on top of generative AI models, evaluate the quality of those applications, and decide whether new models or frameworks will be useful. These are the resources that I personally used for getting up to speed with generative AI. AI foundation Let's start first with the long-form content: books and videos that gave me a more solid foundation. AI Engineering By Chip Huyen This book is a fantastic high-level overview of the AI Engineering industry from an experienced ML researcher. I recommend that everybody read this book at some point in your learning journey. Despite Chip's background in ML, the book is very accessible - no ML background is needed, though a bit of programming with LLMs would be a good warm-up for the book. I loved how Chip included both research and industry insights, and her focus on the need for evaluation in the later chapters. Please, read this book! Build a Large Language Model By Sebastian Raschka This book is a deep dive into building LLMs from scratch using Python and Pytorch, and includes a GitHub repository with runnable code. I found it helpful to see that LLMs are all about matrix manipulation, and to wrap my head around how the different layers in the LLM architecture map to matrices. I recommend it to Python developers who want to understand concepts like the transformer architecture, or even just common LLM parameters like temperature and top p. If you're new to Pytorch, this book thankfully includes an intro in the appendix, but I also liked the Deep Learning with PyTorch book. Zero to Hero By Andrej Karpathy This video series builds neural networks from scratch, entirely in Jupyter notebooks. Andrej is a fantastic teacher, and has a great way of explaining complex topics. Admittedly, I have not watched every video from start to finish, but every time I do watch a video from Andrej, I learn so much. Andrej also gives great ta