A free, structured AI learning website
Learn AI by making
better decisions.
Start from the basics, then move through 19 learning paths, 398 free notes, and hands-on exercises for models, prompts, agents, RAG, and AI engineering.
Start with the work in front of you. The right route is waiting on the other side.
RECOMMENDED ROUTES
Choose your starting point
THE QUESTION THIS PAGE ANSWERS
ANSWER FIRSTHow should a beginner learn AI in a practical, structured way?
Start with what models can do and why they fail, then practice clear prompts and acceptance criteria. Move into agents, RAG, evaluation, cost, or engineering only when the work requires it, testing each step with a small experiment.
If you do not yet have a concrete goal, take the foundations route. If you already have a task, enter through the topic that changes that work.
Read “Choose the job you want AI to help with” first, then choose the shortest useful route.
Do not confuse tracking new models with learning progress. Being able to explain a failure and improve the result is the stronger test.
Learning paths
Choose the work, then take the shortest useful route.
You do not need to read the catalog in order. Name the task in front of you, then follow the notes that explain its important trade-offs.
Read This Before You Build
A short orientation for choosing a useful starting point, building a study habit, and understanding why model fundamentals save time later. Read it when the AI landscape feels noisy or every tool looks equally urgent.
Learning With AI, Deliberately
A practical learning loop for using AI as a tutor without outsourcing your judgment: ask sharper questions, expose weak claims, break difficult material into pieces, and prove what you understood.
AI Without the Fog
A plain-language first pass through what AI can do, how it produces answers, why it can sound certain while being wrong, and what is safe to hand over. No math required; the goal is a dependable first instinct.
The Model Under the Product
Trace the path from training data and token prediction to chat interfaces, hallucinations, and mitigation choices. This chapter gives product decisions a technical reason instead of a trend-driven guess.
The Harness Around the Model
Learn how context, prompts, tools, retrieval, output formats, and safety checks turn a model into a working system. Treat the harness as product architecture, not as a bag of prompt tricks.
From Working Demo to Useful Product
Follow the decisions that separate an impressive demo from a dependable product: interaction loops, context budgets, memory, permissions, multi-agent collaboration, and recovery when the model loses the thread.
Question map
Follow one question across the stack.
Five routes turn a large library into a smaller next step: everyday use, professional leverage, product decisions, hands-on building, or the full map. Pick a job first; let the route choose the theory.
The same question forks two ways, depending on whether you give it constraints. Its default answer works for the most people — so it covers nobody's actual edges.
It doesn't know what you already know, so it pulls a source domain from the public question bank. Leave that slot empty in the question, fill it with something you already get — then the mapping lands on experience you can check.
Numbers, timelines, names and parameters, obscure materials, whether a feature exists — treat these five as suspect by default. In a paragraph that reads smoothly, the invented bits almost always land in these five spots.
Decide by the stakes whether to leave the chat and check the source. The smoother and more complete the answer, the more you should pull one claim and check it against the source.
Six ordinary scenes show where AI changes the shape of work: turning rough notes into structure, explaining specialist material, rehearsing decisions, and making a first version. Start from the outcome you want to move.
See how a vague request changes when you add the situation, the desired result, and the boundaries. The lesson turns prompting from a talent contest into a small specification exercise.
Assemble context, request, constraints, and output format into a brief you can inspect. The useful skill is not finding a secret phrase; it is making the intended result easier to evaluate.
Selected notes
Short notes for long decisions.
Start with outcomes, not AI features
Six ordinary scenes show where AI changes the shape of work: turning rough notes into structure, explaining specialist material, rehearsing decisions, and making a first version. Start from the outcome you want to move.
Interactive practice
Turn a vague request into a brief you can test.
Separate the goal, context, format, and acceptance bar. The answer becomes something you can compare and review, not just something that sounds good.
INTERACTIVE PRACTICE
Turn a vague request into a useful prompt
Clarify the goal, context, and constraints, then carry the finished prompt into the AI tool you use.
TRENDING RANKING
AI notes worth reading next
It doesn't know what you already know, so it pulls a source domain from the public question bank. Leave that slot empty in the question, fill it with something you already get — then the mapping lands on experience you can check.
96read signal↗02Make Knowledge Stick · VerificationLet AI mark the five kinds of info it most likely inventsNumbers, timelines, names and parameters, obscure materials, whether a feature exists — treat these five as suspect by default. In a paragraph that reads smoothly, the invented bits almost always land in these five spots.
96read signal↗03What Exactly Is AI · InteractiveStart with outcomes, not AI featuresSix ordinary scenes show where AI changes the shape of work: turning rough notes into structure, explaining specialist material, rehearsing decisions, and making a first version. Start from the outcome you want to move.
96read signal↗04What Exactly Is AI · ConceptWhy an answer engine is not a source of truthCompare search with generative answers and learn the three boundaries that matter in practice: mixed-up facts, stale knowledge, and missing sources. The habit to keep is knowing when to leave the chat and verify.
96read signal↗05Using It Well · InteractiveChoose a model by job, region, and riskMap global and Chinese model families to the work they are suited for, then compare access, language fit, latency, cost, privacy, and operational control. Model choice is a portfolio decision, not a fan vote.
96read signal↗NEXT STEP / OPEN LEARNING
Leave with a next step.
When the next AI problem arrives, begin with the route that meets the work in front of you.