01
AI Foundations
Understand what AI is, how systems learn, where they fail, and how to judge claims without hype.
Free AI library
Technical guidesFree forever.
177 plain-English guides, structured learning paths, and an open library — built by an independent 501(c)(3) nonprofit so anyone can understand modern AI.
177Free guides
1Topic tracks
~2 minPer guide
~6hReading time
Start here
Five outcome-based courses
Each course includes explicit outcomes, mapped competencies, practice activities, and an applied capstone.
02
Responsible AI User
Use AI productively while protecting privacy, checking outputs, and preserving human accountability.
03
AI at Work
Evaluate workplace use cases, run safe pilots, measure value, and communicate changes responsibly.
04
AI Policy & Society
Analyze AI systems through rights, equity, governance, safety, and public-interest outcomes.
05
Building with AI Systems
Understand language models, retrieval, agents, evaluation, cost, and deployment safeguards through practical system design.
Topic tracks
Browse by track
Jump into the area you care about. Every track has multiple plain-English guides.
FundamentalsCore concepts: how AI works at a high level.84 guides Language AILLMs, NLP, ChatGPT, prompting, and text understanding.155 guides Visual AIImage generation, computer vision, video, and design.133 guides Audio AIVoice, music, podcasting, and audio-first AI.117 guides CompaniesProfiles of OpenAI, Anthropic, Google, Meta, and more.121 guides ApplicationsHow AI shows up in coding, search, agents, and daily life.111 guides IndustriesAI in healthcare, finance, education, energy, and more.91 guides TechnicalChips, fine-tuning, RAG, vectors, and AI infrastructure.177 guides SocietyBias, privacy, copyright, safety, and the AI future.30 guides
Full library
All guides
177 of 1019 guides shown. Filter by track or search above.
Technical
Model Drift Detection
Model drift detection is the practice of monitoring a deployed machine learning model to catch when its accuracy quietly degrades because the real world has…
TechnicalA/B Testing for ML Models
A/B testing for ML models means routing live traffic to two model versions at once and measuring which one actually performs better on real users and real…
TechnicalModel Serialization Formats
Model serialization is how a trained machine learning model gets saved to disk so it can be loaded and run later, on a different machine or in a different…
TechnicalMixed Precision Training
Mixed precision training speeds up neural network training and cuts memory use by performing most math in 16-bit floating point instead of 32-bit.
TechnicalSpeculative Decoding
Speculative decoding makes large language models generate text faster by using a small, fast 'draft' model to guess several tokens ahead, then having the big…
TechnicalKV Cache Optimization
The KV cache stores the keys and values a transformer has already computed so it doesn't redo work for every new token — but it can balloon to gigabytes.
TechnicalFlash Attention
Flash Attention is a clever way to compute the attention step inside Transformers without ever writing the giant attention matrix to slow memory.
TechnicalModel Pruning
Model pruning shrinks a neural network by removing weights or whole structures that contribute little to its output.
TechnicalTokenization and Byte Pair Encoding
Tokenization splits text into the small units a language model actually reads, and Byte Pair Encoding (BPE) is the popular method for building…
Finished reading? Prove it.
Check what you learned with topic quizzes, then explore our structured courses and current certification requirements. Core guides remain free to read.