loganthorneloe/ai-for-software-engineers
learn.aiforswes.comAI foundations for software engineers.
AI Foundations for Software Engineers
AI needs more software engineers.
Recent advancements in modern AI have made it one of the most life-changing technologies of our time, but it can only be effective if applied successfully. This repo contains the resources software engineers need to build a foundational understanding of AI.
It's split into five sections intended to be worked in order:
The guide follows these principles:
- A solid foundation is most important. Technologies used to build systems change over time, but their foundations don't. Building a solid foundation makes learning everything else easier.
- Simplicity is king. This learning roadmap is intentionally lean, while its technical resources go deep into topics. It's purpose-built to help busy professionals build intuition for how AI systems function without wasting time.
- You don't understand what you can't build. Many resources contain hands-on guides. Getting into the code is the best (and most fun) way to understand a topic and the only way to gain practical engineering experience.
This guide assumes the reader has prior experience with programming. Feel free to skip around between resources if you have a background in some topics but not others. Don't treat this as a definitive list of AI resources—there are many excellent AI learning resources. I share great resources as I find them on X.
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Enjoy! 🚀
General Resources
General resources for software design and technical judgment that help when engineering AI systems. Consider these optional.
The Art of Doing Science and Engineering: Learning to Learn by Richard W. Hamming
Two of the most important things to understand as an engineer working in AI are that AI is fundamentally a research discipline and that continued learning matters as much in AI as it does in software engineering. This book helps readers understand both. There is also a shorter essay version of this you can read if you prefer.A Philosophy of Software Design by John Ousterhout
This teaches a useful way to think about software design. It is particularly helpful for managing system complexity, an increasingly important consideration as AI makes software systems more complex.
ML Foundations
Resources for understanding the foundations of machine learning from the bottom up.
The Hundred-Page Machine Learning Book by Andriy Burkov
This is the best short overview of machine learning. It goes into enough depth for the reader to get something substantial out of it without making the topics unapproachable. It's a great way to get started. After reading this, you'll have a good sense of whether ML/AI is something you would enjoy. The book has an accompanying repo.Hands-On Machine Learning with Scikit-Learn and PyTorch by Aurélien Géron
This book is the gold standard for getting hands-on with machine learning. You'll build a machine learning project end-to-end and get hands-on experience with the most important technologies and techniques in AI. Unlike the book above, this goes into great depth but covers many important topics in a single resource. The accompanying repo includes notebooks and exercise solutions.Mathematics of Machine Learning by Tivadar Danka
This book teaches the linear algebra, probability, calculus, and optimization foundations that make models easier to reason about. This is another great resource that covers everything you need to know about ML math in a single book. Most engineers skip this part, but as AI advances, an understanding of the math behind it becomes more important. The book has an accompanying repo.
LLMs
Resources for understanding large language models, the most important topic in modern AI.
The Illustrated Transformer by Jay Alammar, with Transformer Series by 3Blue1Brown as the deeper visual companion
The transformer architecture is the most important concept to grasp when learning how LLMs work. Both resources teach it visually.Build a Large Language Model (From Scratch) by Sebastian Raschka
This book walks through tokenization, embeddings, attention, transformer blocks, training, and fine-tuning so the pieces of an LLM become concrete. It takes you from zero to training your own LLM. The accompanying repo contains the code and exercises.Build a Reasoning Model (From Scratch) by Sebastian Raschka
Reasoning models have become fundamental to real-world AI applications. This book builds intuition for reasoning models by walking through the pieces needed to create one yourself and showing how the surrounding data, training process, and evaluation loop shape the behavior you see. This resource has an accompanying repo.The RLHF Book by Nathan Lambert
Post-training is what turns a base language model into something that follows instructions and matches user expectations. Reinforcement learning is one important part of post-training. This book teaches preference data, reward modeling, reinforcement learning, and the tradeoffs behind shaping LLM behavior after pretraining. It has an accompanying repo and lecture series.
Engineering
Resources for understanding the ML engineering, AI engineering, infrastructure, and operations work required to bring machine learning into real-world applications.
Designing Machine Learning Systems by Chip Huyen
Production ML is different from traditional software because system behavior depends on data distributions that can shift after launch. This book teaches the full production loop around data, model development, deployment, monitoring, and iteration. Its accompanying repo contains chapter summaries and additional resources.AI Engineering by Chip Huyen
Building applications with foundation models introduces additional nondeterminism and complexity. This book teaches the application-layer concepts—context, evals, agents, retrieval, and more—required to build reliable AI applications. Its accompanying repo includes chapter summaries, examples, and case studies.Designing Data-Intensive Applications by Martin Kleppmann
AI systems are data-intensive systems and require strong engineering to solve difficult data-system problems. This book is the gold standard for understanding these challenges and teaches storage, indexing, streams, replication, consistency, and distributed system tradeoffs. This book isn't AI-focused but teaches many required engineering concepts for building AI systems at scale.Building Effective Agents by Anthropic
Agent engineering is in high demand and an excellent way to apply LLMs in a way that's actually helpful. This short read teaches when a workflow is enough, when an agent is actually useful, and the practical patterns for tool use and orchestration. Anthropic provides companion implementation notebooks.Inference Engineering by Philip Kiely
Serving machine learning models at scale is an incredible engineering feat. It requires an understanding of model architecture, serving technologies, and serving hardware to optimize serving systems for latency, reliability, and cost. This is one of the strongest current opportunities for software engineers in AI, and this book does the best job of laying out the information and making it easily understandable.How to Scale Your Model by Google DeepMind
Training large models is another incredible engineering feat and is as much a systems problem as a modeling problem. This online book builds intuition around accelerator parallelism, memory limits, communication costs, and the engineering tradeoffs behind scaling training efficiently. The source repo is available on GitHub.
Hands-on Guides
Hands-on guides from the AI for Software Engineers newsletter that help you work directly with important AI technologies. More coming soon!
Run a Local Coding Model
December 20, 2025 · Repository
Running a model locally is a good way to make open-model tradeoffs concrete. This guide teaches how to set up a local coding model, what the experience feels like compared to hosted coding assistants, and where local inference is useful.Build a Simple Recommendation System
November 11, 2025 · Repository
Recommendation systems are a small but useful way to see ML ideas in code. This guide teaches collaborative filtering and makes features, similarity, sparse data, and user-item interaction data concrete.
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