AI moves fast. Learn it one step at a time.
Find a clear route from basic concepts to practical AI skills.
Learning AI can be confusing when tutorials skip the basics. This map helps you decide where to start, what to learn first, and what to study next.
Explore 150 tutorials across 17 knowledge layers, with 16 interactive demonstrations. Choose a learning route or start with a short lesson.
Give your learning a clear order
Scattered learning
- Complex terms appear before the basics
- Tools are introduced without explaining the problems they solve
- Every article written as if you already read the rest of the pile
- No signal for whether you have the background yet
- You do not know what to read next, so you stop
A guided route
- Ordered by what genuinely depends on what
- Every page shows its layer, its prerequisites, and its next step
- Three routes: use them well, build with them, or know how they work
- The map checks that preparation links exist
- You always know exactly where you are
Three routes, one map
Start with your current level, then choose a direction for your goals. Time estimates are a guide, not a deadline.
The 17 layers
From core concepts to advanced techniques. Follow a recommended route without studying every layer.
- 1 · Entry4
The AI Jargon Decoder · In What Order Should You Learn AI? · What AI Can and Cannot Do (Yet) · +1 more
- 2 · ML Foundations12
Probability for AI · What Is Machine Learning? · How Data Becomes Numbers · +9 more
- 3 · Classic NLP8
Recurrent Neural Networks · LSTM and GRU: Memory That Lasts · Sequence to Sequence and Encoder-Decoder · +5 more
- 4 · Foundations13
Logits and Softmax · What Is a Token in AI? · What Is an Embedding? · +10 more
- 5 · Internals13
Decoding Strategies: Greedy Search and Beam Search · The Geometry of Meaning · How Does a Transformer Work? · +10 more
- 6 · Prompting10
What Is a Context Window? · Why Do AI Models Hallucinate? · Prompt Basics · +7 more
- 7 · RAG7
Why RAG Exists · How RAG Works, Step by Step · How to Split Documents for RAG · +4 more
- 8 · Agents9
What Is an AI Agent? · Agent Architectures: From Rules to Autonomy · How Does Tool Calling Work? · +6 more
- 9 · Application6
Your First LLM API Call · How Do You Know It Works? · From Demo to Production · +3 more
- 10 · Evaluation11
The Bias-Variance Tradeoff · Why Evaluation Is Harder Than Training · Benchmark Contamination and Goodhart's Law · +8 more
- 11 · Multimodal8
How Do Models See Images? · How Image Generation Works · Multimodal Embeddings · +5 more
- 12 · Training and Adaptation12
How Fine-Tuning Actually Works · Model Distillation · Hyperparameter Tuning · +9 more
- 13 · Advanced Architecture8
Attention Variants: Sparse, Linear, and Sliding Window · How Embedding Models Are Trained · How Inference Serving Works · +5 more
- 14 · Advanced RAG7
Agentic RAG · Contextual Retrieval · GraphRAG · +4 more
- 15 · Advanced Agents7
Evaluating Agents · Agent Memory · Coding Agents · +4 more
- 16 · Security7
Model Supply Chain Risk · Handling PII and Sensitive Data · AI Governance and Compliance · +4 more
- 17 · Production8
Latency Optimization · Observability for LLM Systems · Cost Monitoring and Attribution · +5 more
Start with one small question
How does AI turn text into units it can process? Meet tokens, then build your understanding of language models.