1
AI, machine learning, and language models
- AI
- Machine learning
- Language models
- Machine learning
Distinguish a broad field from one kind of model.
AI textbook
Go up one stage at a time: what AI is, how to ask it, what happens inside a language model, and how to build and test your own. Each stage has a short lesson, a practice tool and a self-check.
My LearningWhere are you in the 9 steps?
- Beginner Use AI and check its answers
- Intermediate Look inside a language model
- Advanced Build and evaluate with Python
- Level 1 AI, machine learning, and language models
- Level 2 Write and check a prompt
- Level 3 Text, tokens, and IDs
- Level 4 Vectors and embeddings
- Level 5 Training, inference, and retrieval
- Level 6 Instructions, sampling, and verification
- Level 7 Python: run, inspect, change
- Level 8 Numbers, predictions, and activations
- Level 9 Train and evaluate a small model
Beginner
Use AI and check its answers
No coding needed-
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2
Write and check a prompt
- Task
- Context
- Constraints
- How to check
State the task, useful context, constraints, and a way to check the result.
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3
Text, tokens, and IDs
- Text
- Tokens
- Token IDs
Separate text pieces from their numeric identifiers.
Intermediate
Look inside a language model
Follow the examples at your own pace-
4
Vectors and embeddings
- Token ID
- One-hot vector
- Learned embedding
Distinguish IDs, one-hot vectors, and learned embeddings.
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5
Training, inference, and retrieval
- Training
- Weights
- Inference
Explain what changes in training and what a model uses at inference time.
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6
Instructions, sampling, and verification
- Instructions
- Sampling
- Verification
Compare generation settings without confusing consistency with factual accuracy.
Advanced
Build and evaluate with Python
Small steps, real code-
7
Python: run, inspect, change
- Variables
- Conditions
- Functions
Use variables, conditions, and functions in the browser.
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8
Numbers, predictions, and activations
- Input numbers
- Activation function
- Prediction
Build small functions and explain what their outputs mean.
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9
Train and evaluate a small model
- Split the data
- Train
- Evaluate
Separate training from evaluation and explain the limits of a score.